Heart_Disease_NN.ipynb

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        "<a href=\"https://colab.research.google.com/github/pockypoem/ai-projects/blob/main/Heart_Disease_NN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "**Heart Disease Menggunakan Neural Network**"
      ],
      "metadata": {
        "id": "hI8Z-B_Rj5IM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Import Library**"
      ],
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        "id": "otPLaYFFkPjb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "X5V2u9eqkOrW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Load Dataset**"
      ],
      "metadata": {
        "id": "ovYLnVMGlY48"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"heart.csv\")"
      ],
      "metadata": {
        "id": "UKcaLCAzkxD5"
      },
      "execution_count": 2,
      "outputs": []
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              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
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              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
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              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71f79b23-fd87-4250-b7ba-8f528f2b9568')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-71f79b23-fd87-4250-b7ba-8f528f2b9568 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71f79b23-fd87-4250-b7ba-8f528f2b9568');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Data Exploratory**"
      ],
      "metadata": {
        "id": "LgHanaydmAlu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataset['target']\n",
        "sns.countplot(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "gKWQPMaKlj46",
        "outputId": "212df648-549b-4a46-bee8-44af1158bf00"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='target', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(dataset['sex'], y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "F4l2P6ikmlH7",
        "outputId": "7c7d4c3a-1061-4967-e28e-fa4645f74794"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='sex', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(dataset['thal'], y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "9aNWpdNSm3o5",
        "outputId": "5696954e-dc81-4b22-fbc5-1da0197bf924"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='thal', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Train Test Split**"
      ],
      "metadata": {
        "id": "2A-Nuqpbnshd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = dataset.drop(['target'], axis=1).values\n",
        "y = dataset['target']"
      ],
      "metadata": {
        "id": "Fb1Ey-3inctu"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WN-Uuez5oFBf",
        "outputId": "b71a0988-5608-433d-f980-7b8fe2983943"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[63.,  1.,  3., ...,  0.,  0.,  1.],\n",
              "       [37.,  1.,  2., ...,  0.,  0.,  2.],\n",
              "       [41.,  0.,  1., ...,  2.,  0.,  2.],\n",
              "       ...,\n",
              "       [68.,  1.,  0., ...,  1.,  2.,  3.],\n",
              "       [57.,  1.,  0., ...,  1.,  1.,  3.],\n",
              "       [57.,  0.,  1., ...,  1.,  1.,  2.]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zx541qlxoFcA",
        "outputId": "ed4e8118-2bcd-4751-85ed-9f5bdf8eacf0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "298    0\n",
              "299    0\n",
              "300    0\n",
              "301    0\n",
              "302    0\n",
              "Name: target, Length: 303, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "HOUV7mQGoHE-"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Model Fitting - Machine Learning**"
      ],
      "metadata": {
        "id": "wEsYslcnomBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "Rn_rUUUooVf0"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "lr.fit(X_train, y_train)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuP9Kkeoo8Qb",
        "outputId": "a93ca00d-743b-49de-f102-5b4c660c4e9c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. Metrics Evaluation - ML**"
      ],
      "metadata": {
        "id": "eE95_vU2pnyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score"
      ],
      "metadata": {
        "id": "6OLkDRRPpTuw"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lr_train = lr.predict(X_train)\n",
        "y_pred_lr_test = lr.predict(X_test)\n",
        "\n",
        "y_pred_knn_train = knn.predict(X_train)\n",
        "y_pred_knn_test = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "WxR6AP2GqCIQ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_knn_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dP7M_dLQ2u8T",
        "outputId": "84d5d688-468d-41a1-e1be-7ce18c91aafa"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Logistic Regression \", accuracy_score(y_train, y_pred_lr_train))\n",
        "print(\"Accuracy (test) Logistic Regression \", accuracy_score(y_test, y_pred_lr_test))\n",
        "\n",
        "print(\"Accuracy (train) KNN \", accuracy_score(y_train, y_pred_knn_train))\n",
        "print(\"Accuracy (test) KNN \", accuracy_score(y_test, y_pred_knn_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSnT_wbQqLxW",
        "outputId": "6a6bb390-1ec0-4fae-a474-b3a270b9c3f3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Logistic Regression  0.859504132231405\n",
            "Accuracy (test) Logistic Regression  0.8524590163934426\n",
            "Accuracy (train) KNN  0.7768595041322314\n",
            "Accuracy (test) KNN  0.6557377049180327\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan evaluasi logistic regression, Hasil akurasi dari prediksi data latih dan data uji hampir sama di kisaran 0.85 hanya selisih sedikit saja.\n",
        "\n",
        "Lalu berdasarkan hasil evaluasi KNN, dapat disimpulkan bahwa klasifikasi menggunakan Logistic Regression memiliki skor akurasi yang lebih tinggi dari KNN. Dan pada KNN sendiri pun akurasi pada data latih lebih tinggi daripada data testing 77.6% > 65.5% sehingga dapat dikatakan bahwa model knn mengalami overfitting."
      ],
      "metadata": {
        "id": "hJnUhZ-3rFwk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. Model Fitting - Neural Network**"
      ],
      "metadata": {
        "id": "uCsX3zuEtTmt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential # Sequential berarti proses fully connected itu dilakuakn secara terurut\n",
        "from keras.layers import Dense # istilah hidden layer itu dinamakans sebagai Dense"
      ],
      "metadata": {
        "id": "cHNyxCL6qmRv"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"input dimension: \", X.shape)\n",
        "print(\"output dimension: \", y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFRWPun-xLwX",
        "outputId": "b13ff0a9-2cf6-4c7d-e6a6-a5c0e37605ce"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input dimension:  (303, 13)\n",
            "output dimension:  (303,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build Model with NN**"
      ],
      "metadata": {
        "id": "feqbBTxpyblq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percobaan Pertama: Arsitektur NN dengan 2 Layer\n",
        "\n",
        "Accuracy (train) Neural Network  0.6900826446280992 <br>\n",
        "Accuracy (test) Neural Network  0.7377049180327869"
      ],
      "metadata": {
        "id": "IgcYho5t5w4V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(11, activation='relu', input_dim=13)) # untuk setiap Dense, common activation = Relu\n",
        "model.add(Dense(1, activation='sigmoid')) #untuk setiap output classification: sigmoid (binary) / softmax (multi-binary)"
      ],
      "metadata": {
        "id": "vsOhLMjGyafc"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvatEwgmxmsS",
        "outputId": "c4331a8b-72ea-4b28-ab87-bdaba3c98897"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 11)                154       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 12        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 166\n",
            "Trainable params: 166\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Optimizer Model**"
      ],
      "metadata": {
        "id": "HDm5yXTGzTVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "JTtmbfS8y2SU"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDY0LmqhzmZc",
        "outputId": "a9e47d0d-d408-45f0-9245-c6bf5d50fd10"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6401 - accuracy: 0.6405\n",
            "Epoch 2/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6205 - accuracy: 0.6777\n",
            "Epoch 3/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6223 - accuracy: 0.6694\n",
            "Epoch 4/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6152 - accuracy: 0.6570\n",
            "Epoch 5/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6246 - accuracy: 0.6612\n",
            "Epoch 6/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6371 - accuracy: 0.6653\n",
            "Epoch 7/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6243 - accuracy: 0.6694\n",
            "Epoch 8/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6382 - accuracy: 0.6529\n",
            "Epoch 9/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6185 - accuracy: 0.6736\n",
            "Epoch 10/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6188 - accuracy: 0.6694\n",
            "Epoch 11/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6218 - accuracy: 0.6405\n",
            "Epoch 12/300\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6453 - accuracy: 0.6612\n",
            "Epoch 13/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6252 - accuracy: 0.6322\n",
            "Epoch 14/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6565 - accuracy: 0.6529\n",
            "Epoch 15/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7485 - accuracy: 0.4545\n",
            "Epoch 16/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7356 - accuracy: 0.4545\n",
            "Epoch 17/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7329 - accuracy: 0.4545\n",
            "Epoch 18/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7309 - accuracy: 0.4545\n",
            "Epoch 19/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7291 - accuracy: 0.4545\n",
            "Epoch 20/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7275 - accuracy: 0.4545\n",
            "Epoch 21/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7260 - accuracy: 0.4545\n",
            "Epoch 22/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7245 - accuracy: 0.4545\n",
            "Epoch 23/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7232 - accuracy: 0.4545\n",
            "Epoch 24/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.7217 - accuracy: 0.4545\n",
            "Epoch 25/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7205 - accuracy: 0.4545\n",
            "Epoch 26/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.7191 - accuracy: 0.4545\n",
            "Epoch 27/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7179 - accuracy: 0.4545\n",
            "Epoch 28/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7167 - accuracy: 0.4545\n",
            "Epoch 29/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7157 - accuracy: 0.4545\n",
            "Epoch 30/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7147 - accuracy: 0.4545\n",
            "Epoch 31/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7138 - accuracy: 0.4545\n",
            "Epoch 32/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7128 - accuracy: 0.4545\n",
            "Epoch 33/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7119 - accuracy: 0.4545\n",
            "Epoch 34/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7110 - accuracy: 0.4545\n",
            "Epoch 35/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7101 - accuracy: 0.4545\n",
            "Epoch 36/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7092 - accuracy: 0.4545\n",
            "Epoch 37/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7085 - accuracy: 0.4545\n",
            "Epoch 38/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7077 - accuracy: 0.4545\n",
            "Epoch 39/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7070 - accuracy: 0.4545\n",
            "Epoch 40/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7063 - accuracy: 0.4545\n",
            "Epoch 41/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.7057 - accuracy: 0.4545\n",
            "Epoch 42/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7051 - accuracy: 0.4545\n",
            "Epoch 43/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7044 - accuracy: 0.4545\n",
            "Epoch 44/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7038 - accuracy: 0.4545\n",
            "Epoch 45/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7032 - accuracy: 0.4545\n",
            "Epoch 46/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7027 - accuracy: 0.4545\n",
            "Epoch 47/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7021 - accuracy: 0.4545\n",
            "Epoch 48/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7016 - accuracy: 0.4545\n",
            "Epoch 49/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7010 - accuracy: 0.4545\n",
            "Epoch 50/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7005 - accuracy: 0.4545\n",
            "Epoch 51/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7000 - accuracy: 0.4545\n",
            "Epoch 52/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6996 - accuracy: 0.4545\n",
            "Epoch 53/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6992 - accuracy: 0.4545\n",
            "Epoch 54/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6988 - accuracy: 0.4545\n",
            "Epoch 55/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6983 - accuracy: 0.4545\n",
            "Epoch 56/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4545\n",
            "Epoch 57/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4545\n",
            "Epoch 58/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.4545\n",
            "Epoch 59/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6970 - accuracy: 0.4545\n",
            "Epoch 60/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.4545\n",
            "Epoch 61/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4545\n",
            "Epoch 62/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.4545\n",
            "Epoch 63/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.4545\n",
            "Epoch 64/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6956 - accuracy: 0.4545\n",
            "Epoch 65/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6953 - accuracy: 0.4545\n",
            "Epoch 66/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6950 - accuracy: 0.4545\n",
            "Epoch 67/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4545\n",
            "Epoch 68/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6946 - accuracy: 0.4545\n",
            "Epoch 69/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6943 - accuracy: 0.4545\n",
            "Epoch 70/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6942 - accuracy: 0.4545\n",
            "Epoch 71/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6940 - accuracy: 0.4545\n",
            "Epoch 72/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6938 - accuracy: 0.4545\n",
            "Epoch 73/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6936 - accuracy: 0.4545\n",
            "Epoch 74/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6935 - accuracy: 0.4545\n",
            "Epoch 75/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6933 - accuracy: 0.4545\n",
            "Epoch 76/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6931 - accuracy: 0.4628\n",
            "Epoch 77/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6930 - accuracy: 0.5455\n",
            "Epoch 78/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6928 - accuracy: 0.5455\n",
            "Epoch 79/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6927 - accuracy: 0.5455\n",
            "Epoch 80/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6926 - accuracy: 0.5455\n",
            "Epoch 81/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6925 - accuracy: 0.5455\n",
            "Epoch 82/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5455\n",
            "Epoch 83/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6922 - accuracy: 0.5455\n",
            "Epoch 84/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6921 - accuracy: 0.5455\n",
            "Epoch 85/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6920 - accuracy: 0.5455\n",
            "Epoch 86/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6919 - accuracy: 0.5455\n",
            "Epoch 87/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6917 - accuracy: 0.5455\n",
            "Epoch 88/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6916 - accuracy: 0.5455\n",
            "Epoch 89/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6915 - accuracy: 0.5455\n",
            "Epoch 90/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6914 - accuracy: 0.5455\n",
            "Epoch 91/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6914 - accuracy: 0.5455\n",
            "Epoch 92/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6913 - accuracy: 0.5455\n",
            "Epoch 93/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6913 - accuracy: 0.5455\n",
            "Epoch 94/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6912 - accuracy: 0.5455\n",
            "Epoch 95/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6911 - accuracy: 0.5455\n",
            "Epoch 96/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6910 - accuracy: 0.5455\n",
            "Epoch 97/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6909 - accuracy: 0.5455\n",
            "Epoch 98/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6908 - accuracy: 0.5455\n",
            "Epoch 99/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6908 - accuracy: 0.5455\n",
            "Epoch 100/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6907 - accuracy: 0.5455\n",
            "Epoch 101/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6906 - accuracy: 0.5455\n",
            "Epoch 102/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6906 - accuracy: 0.5455\n",
            "Epoch 103/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6906 - accuracy: 0.5455\n",
            "Epoch 104/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6905 - accuracy: 0.5455\n",
            "Epoch 105/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6905 - accuracy: 0.5455\n",
            "Epoch 106/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6904 - accuracy: 0.5455\n",
            "Epoch 107/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6904 - accuracy: 0.5455\n",
            "Epoch 108/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6903 - accuracy: 0.5455\n",
            "Epoch 109/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6903 - accuracy: 0.5455\n",
            "Epoch 110/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6902 - accuracy: 0.5455\n",
            "Epoch 111/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.5455\n",
            "Epoch 112/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.5455\n",
            "Epoch 113/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.5455\n",
            "Epoch 114/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6900 - accuracy: 0.5455\n",
            "Epoch 115/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6900 - accuracy: 0.5455\n",
            "Epoch 116/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6900 - accuracy: 0.5455\n",
            "Epoch 117/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6899 - accuracy: 0.5455\n",
            "Epoch 118/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6899 - accuracy: 0.5455\n",
            "Epoch 119/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6899 - accuracy: 0.5455\n",
            "Epoch 120/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6899 - accuracy: 0.5455\n",
            "Epoch 121/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6898 - accuracy: 0.5455\n",
            "Epoch 122/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6898 - accuracy: 0.5455\n",
            "Epoch 123/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5455\n",
            "Epoch 124/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5455\n",
            "Epoch 125/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5455\n",
            "Epoch 126/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5455\n",
            "Epoch 127/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5455\n",
            "Epoch 128/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6896 - accuracy: 0.5455\n",
            "Epoch 129/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6896 - accuracy: 0.5455\n",
            "Epoch 130/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6896 - accuracy: 0.5455\n",
            "Epoch 131/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6896 - accuracy: 0.5455\n",
            "Epoch 132/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 133/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 134/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6896 - accuracy: 0.5455\n",
            "Epoch 135/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 136/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 137/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 138/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 139/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5455\n",
            "Epoch 140/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 141/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 142/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 143/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 144/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 145/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6893 - accuracy: 0.5455\n",
            "Epoch 146/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.5455\n",
            "Epoch 147/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6893 - accuracy: 0.5455\n",
            "Epoch 148/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6893 - accuracy: 0.5455\n",
            "Epoch 149/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6893 - accuracy: 0.5455\n",
            "Epoch 150/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6893 - accuracy: 0.5455\n",
            "Epoch 151/300\n",
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            "Epoch 240/300\n",
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            "Epoch 245/300\n",
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            "Epoch 246/300\n",
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            "Epoch 247/300\n",
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            "Epoch 248/300\n",
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            "Epoch 249/300\n",
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            "Epoch 250/300\n",
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            "Epoch 251/300\n",
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            "Epoch 252/300\n",
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            "Epoch 253/300\n",
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            "Epoch 254/300\n",
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            "Epoch 255/300\n",
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            "Epoch 256/300\n",
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            "Epoch 257/300\n",
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            "Epoch 258/300\n",
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            "Epoch 259/300\n",
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            "Epoch 260/300\n",
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            "Epoch 261/300\n",
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            "Epoch 262/300\n",
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            "Epoch 263/300\n",
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            "Epoch 264/300\n",
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            "Epoch 265/300\n",
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            "Epoch 266/300\n",
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            "Epoch 267/300\n",
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            "Epoch 268/300\n",
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            "Epoch 269/300\n",
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            "Epoch 270/300\n",
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            "Epoch 271/300\n",
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            "Epoch 272/300\n",
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            "Epoch 273/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 274/300\n",
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            "Epoch 275/300\n",
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            "Epoch 276/300\n",
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            "Epoch 277/300\n",
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            "Epoch 278/300\n",
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            "Epoch 279/300\n",
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            "Epoch 280/300\n",
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            "Epoch 281/300\n",
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            "Epoch 282/300\n",
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            "Epoch 283/300\n",
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            "Epoch 284/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 285/300\n",
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            "Epoch 286/300\n",
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            "Epoch 287/300\n",
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            "Epoch 288/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 289/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 290/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 291/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 292/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 293/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 294/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 295/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 296/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 297/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 298/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 299/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 300/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train = model.predict(X_train)\n",
        "y_pred_nn_test = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LPV8bO613lo",
        "outputId": "c6436bea-b805-42a7-d40a-e6a58a7d3827"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 4ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train = [round(x[0]) for x in y_pred_nn_train]\n",
        "rounded_y_test= [round(x[0]) for x in y_pred_nn_test]"
      ],
      "metadata": {
        "id": "MvoADi7E3q8D"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtWLsdR5325s",
        "outputId": "4f648e83-c06b-4a2c-bdcf-d2f1a8c0708f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train[y_pred_nn_train > 0.5] = 1\n",
        "y_pred_nn_train[y_pred_nn_train < 0.5] = 0"
      ],
      "metadata": {
        "id": "c0gZYCTt2kRM"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLJZnaki3ks1",
        "outputId": "19376684-9a41-49a2-f5cc-2bd0b1c50b48"
      },
      "execution_count": 38,
      "outputs": [
        {
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Neural Network \", accuracy_score(y_train, rounded_y_train))\n",
        "print(\"Accuracy (test) Neural Network \", accuracy_score(y_test, rounded_y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH5hkX3q2EQ1",
        "outputId": "f1562385-8fb9-4507-b3b7-ece195f921f5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Neural Network  0.6900826446280992\n",
            "Accuracy (test) Neural Network  0.7377049180327869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plotting Hasil Evaluasi**"
      ],
      "metadata": {
        "id": "hVf6sv1X4ljV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z2cHrQFm2aIK",
        "outputId": "ed730117-faa8-4d78-88e7-ec1342083eab"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.title('accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ymtINhoM5Cyv",
        "outputId": "43bd68ff-b64e-4603-8bb0-d5fab58df46f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tp7NI7Y_5ZqW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percobaan Kedua: Arsitektur NN dengan 3 Layer\n",
        "\n",
        "Accuracy (train) Neural Network  0.6776859504132231 <br>\n",
        "Accuracy (test) Neural Network  0.7049180327868853"
      ],
      "metadata": {
        "id": "NutPkQYM55MN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(11, activation='relu', input_dim=13)) # untuk setiap Dense, common activation = Relu\n",
        "model.add(Dense(5, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid')) #untuk setiap output classification: sigmoid (binary) / softmax (multi-binary)"
      ],
      "metadata": {
        "id": "ArIRErYY59xR"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lP5PKICM6EFu",
        "outputId": "8cd56b91-641c-498a-d673-9f7fc4ba0e20"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 11)                154       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 60        \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 6         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 220\n",
            "Trainable params: 220\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "dl1FiLis6IZC"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x_Kzato6eXl",
        "outputId": "927b2e84-3236-4654-d473-d68fc11568c6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "8/8 [==============================] - 1s 3ms/step - loss: 1.5894 - accuracy: 0.5413\n",
            "Epoch 2/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6905 - accuracy: 0.5702\n",
            "Epoch 3/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6537 - accuracy: 0.5702\n",
            "Epoch 4/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6568 - accuracy: 0.6074\n",
            "Epoch 5/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6517 - accuracy: 0.6322\n",
            "Epoch 6/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6439 - accuracy: 0.6116\n",
            "Epoch 7/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6466 - accuracy: 0.6322\n",
            "Epoch 8/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6446 - accuracy: 0.6281\n",
            "Epoch 9/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6525 - accuracy: 0.6157\n",
            "Epoch 10/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6517 - accuracy: 0.6281\n",
            "Epoch 11/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6345 - accuracy: 0.6736\n",
            "Epoch 12/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6443 - accuracy: 0.6116\n",
            "Epoch 13/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6406 - accuracy: 0.6322\n",
            "Epoch 14/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6377 - accuracy: 0.6612\n",
            "Epoch 15/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.6570\n",
            "Epoch 16/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6367 - accuracy: 0.6612\n",
            "Epoch 17/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6367 - accuracy: 0.6446\n",
            "Epoch 18/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6367 - accuracy: 0.6446\n",
            "Epoch 19/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6328 - accuracy: 0.6736\n",
            "Epoch 20/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6340 - accuracy: 0.6281\n",
            "Epoch 21/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6488\n",
            "Epoch 22/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6284 - accuracy: 0.6488\n",
            "Epoch 23/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6549 - accuracy: 0.6198\n",
            "Epoch 24/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6294 - accuracy: 0.6777\n",
            "Epoch 25/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6266 - accuracy: 0.6736\n",
            "Epoch 26/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6294 - accuracy: 0.6818\n",
            "Epoch 27/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6567 - accuracy: 0.6240\n",
            "Epoch 28/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6347 - accuracy: 0.6694\n",
            "Epoch 29/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6388 - accuracy: 0.6488\n",
            "Epoch 30/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6374 - accuracy: 0.6736\n",
            "Epoch 31/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6285 - accuracy: 0.6777\n",
            "Epoch 32/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6344 - accuracy: 0.6777\n",
            "Epoch 33/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6264 - accuracy: 0.6694\n",
            "Epoch 34/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6297 - accuracy: 0.6446\n",
            "Epoch 35/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6164 - accuracy: 0.6818\n",
            "Epoch 36/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6328 - accuracy: 0.6446\n",
            "Epoch 37/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6338 - accuracy: 0.6694\n",
            "Epoch 38/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6502 - accuracy: 0.6033\n",
            "Epoch 39/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6364 - accuracy: 0.6198\n",
            "Epoch 40/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6273 - accuracy: 0.6653\n",
            "Epoch 41/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6422 - accuracy: 0.6612\n",
            "Epoch 42/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6168 - accuracy: 0.6694\n",
            "Epoch 43/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6487 - accuracy: 0.6364\n",
            "Epoch 44/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6313 - accuracy: 0.6570\n",
            "Epoch 45/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6345 - accuracy: 0.6736\n",
            "Epoch 46/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6239 - accuracy: 0.6405\n",
            "Epoch 47/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6226 - accuracy: 0.6942\n",
            "Epoch 48/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6308 - accuracy: 0.6653\n",
            "Epoch 49/300\n",
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            "8/8 [==============================] - 0s 5ms/step - loss: 0.6122 - accuracy: 0.6736\n",
            "Epoch 226/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6208 - accuracy: 0.6488\n",
            "Epoch 227/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6057 - accuracy: 0.6777\n",
            "Epoch 228/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6059 - accuracy: 0.6694\n",
            "Epoch 229/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6094 - accuracy: 0.6818\n",
            "Epoch 230/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6133 - accuracy: 0.7025\n",
            "Epoch 231/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6146 - accuracy: 0.6446\n",
            "Epoch 232/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6087 - accuracy: 0.6777\n",
            "Epoch 233/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.5982 - accuracy: 0.7025\n",
            "Epoch 234/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6123 - accuracy: 0.6736\n",
            "Epoch 235/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6252 - accuracy: 0.6198\n",
            "Epoch 236/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6078 - accuracy: 0.6570\n",
            "Epoch 237/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6107 - accuracy: 0.6736\n",
            "Epoch 238/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6182 - accuracy: 0.6612\n",
            "Epoch 239/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6023 - accuracy: 0.6901\n",
            "Epoch 240/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6204 - accuracy: 0.6529\n",
            "Epoch 241/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6142 - accuracy: 0.6901\n",
            "Epoch 242/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6136 - accuracy: 0.6777\n",
            "Epoch 243/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6067 - accuracy: 0.6777\n",
            "Epoch 244/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6229 - accuracy: 0.6736\n",
            "Epoch 245/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6066 - accuracy: 0.6942\n",
            "Epoch 246/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6148 - accuracy: 0.6653\n",
            "Epoch 247/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6216 - accuracy: 0.6612\n",
            "Epoch 248/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6096 - accuracy: 0.6694\n",
            "Epoch 249/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6138 - accuracy: 0.6612\n",
            "Epoch 250/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6074 - accuracy: 0.6529\n",
            "Epoch 251/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6054 - accuracy: 0.6694\n",
            "Epoch 252/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6087 - accuracy: 0.6818\n",
            "Epoch 253/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6129 - accuracy: 0.6529\n",
            "Epoch 254/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6188 - accuracy: 0.6446\n",
            "Epoch 255/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6063 - accuracy: 0.6942\n",
            "Epoch 256/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6027 - accuracy: 0.6818\n",
            "Epoch 257/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6039 - accuracy: 0.6694\n",
            "Epoch 258/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6091 - accuracy: 0.6612\n",
            "Epoch 259/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6039 - accuracy: 0.6736\n",
            "Epoch 260/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6134 - accuracy: 0.6736\n",
            "Epoch 261/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6107 - accuracy: 0.6818\n",
            "Epoch 262/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6011 - accuracy: 0.6653\n",
            "Epoch 263/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6175 - accuracy: 0.6612\n",
            "Epoch 264/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6166 - accuracy: 0.6736\n",
            "Epoch 265/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6077 - accuracy: 0.6942\n",
            "Epoch 266/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6098 - accuracy: 0.6694\n",
            "Epoch 267/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6018 - accuracy: 0.6860\n",
            "Epoch 268/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6145 - accuracy: 0.6488\n",
            "Epoch 269/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6129 - accuracy: 0.6860\n",
            "Epoch 270/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6075 - accuracy: 0.6777\n",
            "Epoch 271/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6110 - accuracy: 0.6736\n",
            "Epoch 272/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6092 - accuracy: 0.6653\n",
            "Epoch 273/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6056 - accuracy: 0.6736\n",
            "Epoch 274/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6155 - accuracy: 0.6736\n",
            "Epoch 275/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6018 - accuracy: 0.6983\n",
            "Epoch 276/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6024 - accuracy: 0.6694\n",
            "Epoch 277/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6023 - accuracy: 0.6942\n",
            "Epoch 278/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6055 - accuracy: 0.6736\n",
            "Epoch 279/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6051 - accuracy: 0.6653\n",
            "Epoch 280/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6054 - accuracy: 0.6529\n",
            "Epoch 281/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6095 - accuracy: 0.6860\n",
            "Epoch 282/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6132 - accuracy: 0.6777\n",
            "Epoch 283/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6132 - accuracy: 0.6612\n",
            "Epoch 284/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6006 - accuracy: 0.6818\n",
            "Epoch 285/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6258 - accuracy: 0.6446\n",
            "Epoch 286/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6092 - accuracy: 0.6653\n",
            "Epoch 287/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6021 - accuracy: 0.6736\n",
            "Epoch 288/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6071 - accuracy: 0.6860\n",
            "Epoch 289/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6026 - accuracy: 0.6736\n",
            "Epoch 290/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6288 - accuracy: 0.6488\n",
            "Epoch 291/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6099 - accuracy: 0.6942\n",
            "Epoch 292/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6066 - accuracy: 0.6777\n",
            "Epoch 293/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6107 - accuracy: 0.6777\n",
            "Epoch 294/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6058 - accuracy: 0.6777\n",
            "Epoch 295/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5992 - accuracy: 0.7025\n",
            "Epoch 296/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6114 - accuracy: 0.6653\n",
            "Epoch 297/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6085 - accuracy: 0.6818\n",
            "Epoch 298/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6018 - accuracy: 0.6901\n",
            "Epoch 299/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6140 - accuracy: 0.6694\n",
            "Epoch 300/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6267 - accuracy: 0.6446\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train = model.predict(X_train)\n",
        "y_pred_nn_test = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwE8EEvU6uVs",
        "outputId": "3d73962a-1598-40d1-c669-e1ba39737e38"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train = [round(x[0]) for x in y_pred_nn_train]\n",
        "rounded_y_test= [round(x[0]) for x in y_pred_nn_test]"
      ],
      "metadata": {
        "id": "D7YMUGy57CJ_"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Neural Network \", accuracy_score(y_train, rounded_y_train))\n",
        "print(\"Accuracy (test) Neural Network \", accuracy_score(y_test, rounded_y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTro11FU7EKV",
        "outputId": "524a194d-8d97-4028-be3b-e3f1491e8c81"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Neural Network  0.6776859504132231\n",
            "Accuracy (test) Neural Network  0.7049180327868853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U4doacrH7Fk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percobaan Ketiga: Arsitektur NN dengan 4 Layer\n",
        "\n",
        "**Optimizer = Stochastic Gradient Descent** <br>\n",
        "Accuracy (train) Neural Network  0.5454545454545454 <br>\n",
        "Accuracy (test) Neural Network  0.5409836065573771\n",
        "\n",
        "<br>\n",
        "\n",
        "**Optimizer = Adam** <br>\n",
        "Accuracy (train) Neural Network  0.5454545454545454 <br>\n",
        "Accuracy (test) Neural Network  0.5409836065573771"
      ],
      "metadata": {
        "id": "xkWYA5gr7x6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(16, activation='relu', input_dim=13)) # untuk setiap Dense, common activation = Relu\n",
        "model.add(Dense(16,activation='relu'))\n",
        "model.add(Dense(8,activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "lX4WYKab73Qc"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MXP9OUd8Bby",
        "outputId": "c2b68af2-3f22-4672-e608-d31f6335b855"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_5 (Dense)             (None, 16)                224       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 16)                272       \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 8)                 136       \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 641\n",
            "Trainable params: 641\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "N9XcehR98Cg8"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5Cpgt1-8Gob",
        "outputId": "3f9730ee-abb2-4d53-dedb-eff96d4b8c4d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "8/8 [==============================] - 1s 4ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 2/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 3/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 4/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 5/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 6/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 7/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 8/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 9/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 10/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 11/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 12/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 13/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 14/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 15/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 16/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 17/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 18/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 19/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 20/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 21/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 22/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 23/300\n",
            "8/8 [==============================] - 0s 50ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 24/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 25/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 26/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 27/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 28/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 29/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 30/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 31/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 32/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 33/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 34/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 35/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 36/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 37/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 38/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 39/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 40/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 41/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 42/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 43/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 44/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 45/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 46/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 47/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 48/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 49/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 50/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 51/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 52/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 53/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 54/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 55/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 56/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 57/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 58/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 59/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 60/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 61/300\n",
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            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 238/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 239/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 240/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 241/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 242/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 243/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 244/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 245/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 246/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 247/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 248/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 249/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 250/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 251/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 252/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 253/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 254/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 255/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 256/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 257/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 258/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 259/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 260/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 261/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 262/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 263/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 264/300\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 265/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 266/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 267/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 268/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 269/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 270/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 271/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 272/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 273/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 274/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 275/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 276/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 277/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 278/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 279/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 280/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 281/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 282/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 283/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 284/300\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 285/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 286/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 287/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 288/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 289/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 290/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 291/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 292/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6891 - accuracy: 0.5455\n",
            "Epoch 293/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 294/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 295/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 296/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 297/300\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 298/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 299/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n",
            "Epoch 300/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6890 - accuracy: 0.5455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train = model.predict(X_train)\n",
        "y_pred_nn_test = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vdjd6MpL8Qyy",
        "outputId": "62a31e0e-e0a2-4861-f568-b901358b3da8"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "2/2 [==============================] - 0s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train = [round(x[0]) for x in y_pred_nn_train]\n",
        "rounded_y_test= [round(x[0]) for x in y_pred_nn_test]"
      ],
      "metadata": {
        "id": "tSZWgACX8fWm"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Neural Network \", accuracy_score(y_train, rounded_y_train))\n",
        "print(\"Accuracy (test) Neural Network \", accuracy_score(y_test, rounded_y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krlZrta38ijx",
        "outputId": "5c0c0bed-a36b-4add-a934-b801a3e194ba"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Neural Network  0.5454545454545454\n",
            "Accuracy (test) Neural Network  0.5409836065573771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zoaJB-bJ8j2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percobaan Keempat: Arsitektur NN dengan Multi Layer Perceptron\n",
        "\n",
        "Accuracy (train) Neural Network  0.731404958677686 <br>\n",
        "Accuracy (test) Neural Network  0.7540983606557377 "
      ],
      "metadata": {
        "id": "oagZm1zl-J41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu', input_dim=13))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "FyK9gYo8-PAE"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "V_b4rbic-V7Y"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9QTU-Ee-ZzX",
        "outputId": "23815c31-4b4a-47bb-f125-d81635ce7d35"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 0s 36ms/step - loss: 0.3479 - accuracy: 0.8554 - val_loss: 0.3874 - val_accuracy: 0.8197\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3327 - accuracy: 0.8636 - val_loss: 0.5186 - val_accuracy: 0.7869\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3591 - accuracy: 0.8264 - val_loss: 0.6937 - val_accuracy: 0.6721\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4189 - accuracy: 0.8306 - val_loss: 0.5137 - val_accuracy: 0.7705\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3911 - accuracy: 0.8471 - val_loss: 0.4448 - val_accuracy: 0.8197\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3838 - accuracy: 0.8140 - val_loss: 0.5147 - val_accuracy: 0.8033\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3986 - accuracy: 0.8223 - val_loss: 0.4142 - val_accuracy: 0.8033\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.4500 - accuracy: 0.7851 - val_loss: 0.4424 - val_accuracy: 0.8197\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3396 - accuracy: 0.8512 - val_loss: 0.6397 - val_accuracy: 0.6721\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3682 - accuracy: 0.8430 - val_loss: 0.5154 - val_accuracy: 0.8033\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3425 - accuracy: 0.8347 - val_loss: 0.5225 - val_accuracy: 0.7869\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3478 - accuracy: 0.8388 - val_loss: 0.4315 - val_accuracy: 0.8197\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3463 - accuracy: 0.8471 - val_loss: 0.4320 - val_accuracy: 0.8361\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3399 - accuracy: 0.8636 - val_loss: 0.4259 - val_accuracy: 0.8033\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3166 - accuracy: 0.8595 - val_loss: 0.4083 - val_accuracy: 0.8033\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3228 - accuracy: 0.8802 - val_loss: 0.4693 - val_accuracy: 0.8197\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3112 - accuracy: 0.8719 - val_loss: 0.4175 - val_accuracy: 0.8197\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3169 - accuracy: 0.8719 - val_loss: 0.4377 - val_accuracy: 0.8361\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3358 - accuracy: 0.8678 - val_loss: 0.4192 - val_accuracy: 0.7869\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3464 - accuracy: 0.8430 - val_loss: 0.4019 - val_accuracy: 0.8525\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3378 - accuracy: 0.8595 - val_loss: 0.4146 - val_accuracy: 0.8033\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3601 - accuracy: 0.8182 - val_loss: 0.4687 - val_accuracy: 0.8197\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4094 - accuracy: 0.7893 - val_loss: 0.4017 - val_accuracy: 0.8197\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4168 - accuracy: 0.8058 - val_loss: 0.6003 - val_accuracy: 0.7213\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5456 - accuracy: 0.7769 - val_loss: 0.6146 - val_accuracy: 0.7213\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5795 - accuracy: 0.7727 - val_loss: 0.7244 - val_accuracy: 0.6885\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6198 - accuracy: 0.7603 - val_loss: 0.4004 - val_accuracy: 0.8361\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4026 - accuracy: 0.8388 - val_loss: 0.4230 - val_accuracy: 0.8033\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3877 - accuracy: 0.8099 - val_loss: 0.4596 - val_accuracy: 0.8361\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3875 - accuracy: 0.8017 - val_loss: 0.4815 - val_accuracy: 0.8197\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3751 - accuracy: 0.8182 - val_loss: 0.4819 - val_accuracy: 0.8033\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3848 - accuracy: 0.8182 - val_loss: 0.5111 - val_accuracy: 0.8197\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3258 - accuracy: 0.8595 - val_loss: 0.5401 - val_accuracy: 0.8197\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3024 - accuracy: 0.8595 - val_loss: 0.4493 - val_accuracy: 0.7869\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3456 - accuracy: 0.8471 - val_loss: 0.4429 - val_accuracy: 0.8197\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3119 - accuracy: 0.8760 - val_loss: 0.4755 - val_accuracy: 0.8197\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3307 - accuracy: 0.8430 - val_loss: 0.4318 - val_accuracy: 0.8197\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3737 - accuracy: 0.8223 - val_loss: 0.4007 - val_accuracy: 0.8197\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3276 - accuracy: 0.8636 - val_loss: 0.4271 - val_accuracy: 0.8361\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3036 - accuracy: 0.8884 - val_loss: 0.4664 - val_accuracy: 0.8197\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3158 - accuracy: 0.8678 - val_loss: 0.6537 - val_accuracy: 0.7213\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3710 - accuracy: 0.8306 - val_loss: 0.4652 - val_accuracy: 0.8197\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3636 - accuracy: 0.8347 - val_loss: 0.4057 - val_accuracy: 0.8361\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3358 - accuracy: 0.8388 - val_loss: 0.4148 - val_accuracy: 0.8525\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3354 - accuracy: 0.8678 - val_loss: 0.4509 - val_accuracy: 0.8197\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3347 - accuracy: 0.8264 - val_loss: 0.4885 - val_accuracy: 0.8197\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4896 - accuracy: 0.7603 - val_loss: 0.5420 - val_accuracy: 0.7869\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4313 - accuracy: 0.8099 - val_loss: 0.5090 - val_accuracy: 0.8033\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3459 - accuracy: 0.8058 - val_loss: 0.4478 - val_accuracy: 0.8197\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3312 - accuracy: 0.8512 - val_loss: 0.5293 - val_accuracy: 0.8197\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3429 - accuracy: 0.8471 - val_loss: 0.4875 - val_accuracy: 0.8197\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3625 - accuracy: 0.8347 - val_loss: 0.5621 - val_accuracy: 0.7705\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3178 - accuracy: 0.8843 - val_loss: 0.5811 - val_accuracy: 0.7869\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3199 - accuracy: 0.8554 - val_loss: 0.4926 - val_accuracy: 0.8361\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3389 - accuracy: 0.8554 - val_loss: 0.4344 - val_accuracy: 0.7869\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3379 - accuracy: 0.8471 - val_loss: 0.4425 - val_accuracy: 0.8197\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3139 - accuracy: 0.8595 - val_loss: 0.4563 - val_accuracy: 0.8197\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3256 - accuracy: 0.8636 - val_loss: 0.4844 - val_accuracy: 0.7869\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.4462 - accuracy: 0.7769 - val_loss: 0.6353 - val_accuracy: 0.7049\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5447 - accuracy: 0.7479 - val_loss: 0.4576 - val_accuracy: 0.8361\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3954 - accuracy: 0.8306 - val_loss: 0.4173 - val_accuracy: 0.8361\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3667 - accuracy: 0.8182 - val_loss: 0.4252 - val_accuracy: 0.8033\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3480 - accuracy: 0.8512 - val_loss: 0.4372 - val_accuracy: 0.8361\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3024 - accuracy: 0.8967 - val_loss: 0.4315 - val_accuracy: 0.8197\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3008 - accuracy: 0.8884 - val_loss: 0.5221 - val_accuracy: 0.8197\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3076 - accuracy: 0.8843 - val_loss: 0.5808 - val_accuracy: 0.8033\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3471 - accuracy: 0.8347 - val_loss: 0.4922 - val_accuracy: 0.8197\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3244 - accuracy: 0.8306 - val_loss: 0.6355 - val_accuracy: 0.7541\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3857 - accuracy: 0.8058 - val_loss: 0.6242 - val_accuracy: 0.7541\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3087 - accuracy: 0.8719 - val_loss: 0.5493 - val_accuracy: 0.8197\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3048 - accuracy: 0.8843 - val_loss: 0.6187 - val_accuracy: 0.7869\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3342 - accuracy: 0.8347 - val_loss: 0.6822 - val_accuracy: 0.7049\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3930 - accuracy: 0.8058 - val_loss: 0.5074 - val_accuracy: 0.8033\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4312 - accuracy: 0.8264 - val_loss: 0.4090 - val_accuracy: 0.8361\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3663 - accuracy: 0.8223 - val_loss: 0.4370 - val_accuracy: 0.8197\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3281 - accuracy: 0.8388 - val_loss: 0.5388 - val_accuracy: 0.8197\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3164 - accuracy: 0.8719 - val_loss: 0.6328 - val_accuracy: 0.7705\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3619 - accuracy: 0.8388 - val_loss: 0.6342 - val_accuracy: 0.7705\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3102 - accuracy: 0.8595 - val_loss: 0.4579 - val_accuracy: 0.8197\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3359 - accuracy: 0.8140 - val_loss: 0.5073 - val_accuracy: 0.8197\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.2933 - accuracy: 0.8760 - val_loss: 0.5029 - val_accuracy: 0.8197\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2879 - accuracy: 0.8760 - val_loss: 0.4855 - val_accuracy: 0.8361\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3143 - accuracy: 0.8512 - val_loss: 0.5202 - val_accuracy: 0.8197\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3103 - accuracy: 0.8760 - val_loss: 0.4263 - val_accuracy: 0.8197\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3535 - accuracy: 0.8430 - val_loss: 0.4375 - val_accuracy: 0.8197\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3247 - accuracy: 0.8554 - val_loss: 0.5199 - val_accuracy: 0.7705\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3810 - accuracy: 0.8099 - val_loss: 0.4334 - val_accuracy: 0.8033\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2950 - accuracy: 0.8554 - val_loss: 0.5029 - val_accuracy: 0.8361\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.2972 - accuracy: 0.8678 - val_loss: 0.6769 - val_accuracy: 0.7377\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3452 - accuracy: 0.8430 - val_loss: 0.6564 - val_accuracy: 0.7049\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3760 - accuracy: 0.8512 - val_loss: 0.5950 - val_accuracy: 0.7869\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2984 - accuracy: 0.8802 - val_loss: 0.5167 - val_accuracy: 0.8361\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2896 - accuracy: 0.8678 - val_loss: 0.4883 - val_accuracy: 0.8197\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3320 - accuracy: 0.8512 - val_loss: 0.4565 - val_accuracy: 0.8197\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3309 - accuracy: 0.8430 - val_loss: 0.4956 - val_accuracy: 0.8361\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2843 - accuracy: 0.8884 - val_loss: 0.4558 - val_accuracy: 0.8197\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.2977 - accuracy: 0.8678 - val_loss: 0.4622 - val_accuracy: 0.8033\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.2999 - accuracy: 0.8802 - val_loss: 0.4393 - val_accuracy: 0.8197\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3004 - accuracy: 0.8843 - val_loss: 0.4709 - val_accuracy: 0.8033\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3242 - accuracy: 0.8719 - val_loss: 0.5576 - val_accuracy: 0.7541\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train = model.predict(X_train)\n",
        "y_pred_nn_test = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uc9PHXEU-109",
        "outputId": "0da425d6-f18f-40f5-ee4b-1f3b103432c4"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "2/2 [==============================] - 0s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train = [round(x[0]) for x in y_pred_nn_train]\n",
        "rounded_y_test= [round(x[0]) for x in y_pred_nn_test]"
      ],
      "metadata": {
        "id": "C4Vft9C_-5nm"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Neural Network \", accuracy_score(y_train, rounded_y_train))\n",
        "print(\"Accuracy (test) Neural Network \", accuracy_score(y_test, rounded_y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PwKUQeb-8gK",
        "outputId": "6bb07cf8-5f9d-4f06-b5b7-1b30d14dd189"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Neural Network  0.731404958677686\n",
            "Accuracy (test) Neural Network  0.7540983606557377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fm3ZwBcc--3L",
        "outputId": "177f86a1-a567-4684-c69d-dedc540912b6"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Jv8MVuxRBoGm",
        "outputId": "82e1d77b-5854-412f-a1e1-749882b825ae"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percobaan Kelima: Arsitektur NN dengan CNN"
      ],
      "metadata": {
        "id": "R1oEbTj3_nxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense"
      ],
      "metadata": {
        "id": "jHX9lcTL_s23"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32, 3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
        "model.add(MaxPooling1D(2))\n",
        "model.add(Conv1D(64, 3, activation='relu'))\n",
        "model.add(MaxPooling1D(2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "bGZIlGML_uu3"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "whdtR6Xk_wmq"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train.reshape(X_train.shape[0], X_train.shape[1], 1), y_train, epochs=140, batch_size=32, validation_data=(X_test.reshape(X_test.shape[0], X_test.shape[1], 1), y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6XrTJHr_41O",
        "outputId": "796fa2d6-2de9-4584-eb66-65b7f2d113b3"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/140\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 0.5564 - accuracy: 0.6901 - val_loss: 0.5718 - val_accuracy: 0.6557\n",
            "Epoch 2/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.6045 - accuracy: 0.6777 - val_loss: 0.5027 - val_accuracy: 0.7541\n",
            "Epoch 3/140\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.5674 - accuracy: 0.7397 - val_loss: 0.4948 - val_accuracy: 0.7869\n",
            "Epoch 4/140\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.5581 - accuracy: 0.6983 - val_loss: 0.4846 - val_accuracy: 0.7705\n",
            "Epoch 5/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5583 - accuracy: 0.6942 - val_loss: 0.4769 - val_accuracy: 0.7705\n",
            "Epoch 6/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5482 - accuracy: 0.6777 - val_loss: 0.5132 - val_accuracy: 0.7705\n",
            "Epoch 7/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5835 - accuracy: 0.6942 - val_loss: 0.4870 - val_accuracy: 0.7869\n",
            "Epoch 8/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5887 - accuracy: 0.6942 - val_loss: 0.5248 - val_accuracy: 0.7377\n",
            "Epoch 9/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5719 - accuracy: 0.6694 - val_loss: 0.4888 - val_accuracy: 0.8197\n",
            "Epoch 10/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5269 - accuracy: 0.7190 - val_loss: 0.4889 - val_accuracy: 0.8361\n",
            "Epoch 11/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5383 - accuracy: 0.7355 - val_loss: 0.5615 - val_accuracy: 0.6557\n",
            "Epoch 12/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5830 - accuracy: 0.6777 - val_loss: 0.4924 - val_accuracy: 0.7869\n",
            "Epoch 13/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5562 - accuracy: 0.7025 - val_loss: 0.4923 - val_accuracy: 0.7705\n",
            "Epoch 14/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5390 - accuracy: 0.7190 - val_loss: 0.4932 - val_accuracy: 0.7541\n",
            "Epoch 15/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5430 - accuracy: 0.7107 - val_loss: 0.5932 - val_accuracy: 0.6557\n",
            "Epoch 16/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7141 - accuracy: 0.6198 - val_loss: 1.1367 - val_accuracy: 0.5410\n",
            "Epoch 17/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.8725 - accuracy: 0.5992 - val_loss: 0.9298 - val_accuracy: 0.5902\n",
            "Epoch 18/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7138 - accuracy: 0.6694 - val_loss: 0.4810 - val_accuracy: 0.7705\n",
            "Epoch 19/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6347 - accuracy: 0.6653 - val_loss: 0.5704 - val_accuracy: 0.6557\n",
            "Epoch 20/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5844 - accuracy: 0.6860 - val_loss: 0.4804 - val_accuracy: 0.8033\n",
            "Epoch 21/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5982 - accuracy: 0.7066 - val_loss: 0.5443 - val_accuracy: 0.7213\n",
            "Epoch 22/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5776 - accuracy: 0.6777 - val_loss: 0.4788 - val_accuracy: 0.7705\n",
            "Epoch 23/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5446 - accuracy: 0.7190 - val_loss: 0.4811 - val_accuracy: 0.7705\n",
            "Epoch 24/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5357 - accuracy: 0.7149 - val_loss: 0.6027 - val_accuracy: 0.6721\n",
            "Epoch 25/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5868 - accuracy: 0.6901 - val_loss: 0.5359 - val_accuracy: 0.7049\n",
            "Epoch 26/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5188 - accuracy: 0.7397 - val_loss: 0.4628 - val_accuracy: 0.8361\n",
            "Epoch 27/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5103 - accuracy: 0.7355 - val_loss: 0.4696 - val_accuracy: 0.7705\n",
            "Epoch 28/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5013 - accuracy: 0.7603 - val_loss: 0.4737 - val_accuracy: 0.8197\n",
            "Epoch 29/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5173 - accuracy: 0.7603 - val_loss: 0.5061 - val_accuracy: 0.7541\n",
            "Epoch 30/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5453 - accuracy: 0.6942 - val_loss: 0.5042 - val_accuracy: 0.7541\n",
            "Epoch 31/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6184 - accuracy: 0.6983 - val_loss: 0.5101 - val_accuracy: 0.7049\n",
            "Epoch 32/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6029 - accuracy: 0.6818 - val_loss: 0.4665 - val_accuracy: 0.7705\n",
            "Epoch 33/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5611 - accuracy: 0.7149 - val_loss: 0.5311 - val_accuracy: 0.7213\n",
            "Epoch 34/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5952 - accuracy: 0.6570 - val_loss: 0.4861 - val_accuracy: 0.7541\n",
            "Epoch 35/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5820 - accuracy: 0.6942 - val_loss: 0.4435 - val_accuracy: 0.8033\n",
            "Epoch 36/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5504 - accuracy: 0.7190 - val_loss: 0.4633 - val_accuracy: 0.7705\n",
            "Epoch 37/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5625 - accuracy: 0.6983 - val_loss: 0.4539 - val_accuracy: 0.7869\n",
            "Epoch 38/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5102 - accuracy: 0.7603 - val_loss: 0.4698 - val_accuracy: 0.7705\n",
            "Epoch 39/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5322 - accuracy: 0.7438 - val_loss: 0.4506 - val_accuracy: 0.7869\n",
            "Epoch 40/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5332 - accuracy: 0.7438 - val_loss: 0.4572 - val_accuracy: 0.8033\n",
            "Epoch 41/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5079 - accuracy: 0.7562 - val_loss: 0.4573 - val_accuracy: 0.8033\n",
            "Epoch 42/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5080 - accuracy: 0.7562 - val_loss: 0.4710 - val_accuracy: 0.8197\n",
            "Epoch 43/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5008 - accuracy: 0.7562 - val_loss: 0.4921 - val_accuracy: 0.7705\n",
            "Epoch 44/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4970 - accuracy: 0.7603 - val_loss: 0.4775 - val_accuracy: 0.8033\n",
            "Epoch 45/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5045 - accuracy: 0.7479 - val_loss: 0.4655 - val_accuracy: 0.8033\n",
            "Epoch 46/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4919 - accuracy: 0.7727 - val_loss: 0.4441 - val_accuracy: 0.7705\n",
            "Epoch 47/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5304 - accuracy: 0.7273 - val_loss: 0.4491 - val_accuracy: 0.7869\n",
            "Epoch 48/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.5234 - accuracy: 0.7314 - val_loss: 0.4495 - val_accuracy: 0.7705\n",
            "Epoch 49/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5208 - accuracy: 0.7810 - val_loss: 0.4545 - val_accuracy: 0.7705\n",
            "Epoch 50/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4954 - accuracy: 0.7686 - val_loss: 0.4571 - val_accuracy: 0.7869\n",
            "Epoch 51/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5268 - accuracy: 0.7355 - val_loss: 0.4537 - val_accuracy: 0.7869\n",
            "Epoch 52/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4766 - accuracy: 0.7769 - val_loss: 0.4534 - val_accuracy: 0.8033\n",
            "Epoch 53/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5039 - accuracy: 0.7397 - val_loss: 0.4739 - val_accuracy: 0.7869\n",
            "Epoch 54/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6011 - accuracy: 0.7066 - val_loss: 0.4697 - val_accuracy: 0.8033\n",
            "Epoch 55/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5881 - accuracy: 0.6694 - val_loss: 0.4631 - val_accuracy: 0.8197\n",
            "Epoch 56/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5566 - accuracy: 0.7107 - val_loss: 0.5692 - val_accuracy: 0.6885\n",
            "Epoch 57/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5298 - accuracy: 0.7438 - val_loss: 0.6243 - val_accuracy: 0.6721\n",
            "Epoch 58/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5494 - accuracy: 0.6983 - val_loss: 0.5104 - val_accuracy: 0.7541\n",
            "Epoch 59/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.6020 - accuracy: 0.6818 - val_loss: 0.4749 - val_accuracy: 0.8033\n",
            "Epoch 60/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5791 - accuracy: 0.7066 - val_loss: 0.4532 - val_accuracy: 0.7705\n",
            "Epoch 61/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4900 - accuracy: 0.7603 - val_loss: 0.4545 - val_accuracy: 0.7705\n",
            "Epoch 62/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5111 - accuracy: 0.7645 - val_loss: 0.4937 - val_accuracy: 0.7705\n",
            "Epoch 63/140\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4939 - accuracy: 0.7769 - val_loss: 0.5243 - val_accuracy: 0.7705\n",
            "Epoch 64/140\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.4821 - accuracy: 0.7686 - val_loss: 0.4530 - val_accuracy: 0.7869\n",
            "Epoch 65/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4930 - accuracy: 0.7562 - val_loss: 0.5422 - val_accuracy: 0.7377\n",
            "Epoch 66/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5295 - accuracy: 0.7355 - val_loss: 0.5393 - val_accuracy: 0.7541\n",
            "Epoch 67/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5007 - accuracy: 0.7397 - val_loss: 0.5073 - val_accuracy: 0.7705\n",
            "Epoch 68/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4804 - accuracy: 0.7810 - val_loss: 0.4959 - val_accuracy: 0.7705\n",
            "Epoch 69/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4742 - accuracy: 0.7851 - val_loss: 0.4566 - val_accuracy: 0.8033\n",
            "Epoch 70/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4709 - accuracy: 0.7769 - val_loss: 0.4970 - val_accuracy: 0.7705\n",
            "Epoch 71/140\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.5089 - accuracy: 0.7438 - val_loss: 0.6828 - val_accuracy: 0.6721\n",
            "Epoch 72/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5427 - accuracy: 0.7231 - val_loss: 0.5111 - val_accuracy: 0.7869\n",
            "Epoch 73/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4886 - accuracy: 0.7686 - val_loss: 0.4639 - val_accuracy: 0.8033\n",
            "Epoch 74/140\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4989 - accuracy: 0.7645 - val_loss: 0.4557 - val_accuracy: 0.8033\n",
            "Epoch 75/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4988 - accuracy: 0.7603 - val_loss: 0.4520 - val_accuracy: 0.7869\n",
            "Epoch 76/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4809 - accuracy: 0.7893 - val_loss: 0.4492 - val_accuracy: 0.7869\n",
            "Epoch 77/140\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4691 - accuracy: 0.7975 - val_loss: 0.4659 - val_accuracy: 0.8033\n",
            "Epoch 78/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4725 - accuracy: 0.7810 - val_loss: 0.5151 - val_accuracy: 0.7705\n",
            "Epoch 79/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4907 - accuracy: 0.7686 - val_loss: 0.5572 - val_accuracy: 0.7213\n",
            "Epoch 80/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5599 - accuracy: 0.7273 - val_loss: 0.4526 - val_accuracy: 0.8033\n",
            "Epoch 81/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4962 - accuracy: 0.7645 - val_loss: 0.4649 - val_accuracy: 0.7869\n",
            "Epoch 82/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5186 - accuracy: 0.7479 - val_loss: 0.4623 - val_accuracy: 0.8033\n",
            "Epoch 83/140\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.5227 - accuracy: 0.7438 - val_loss: 0.4827 - val_accuracy: 0.7869\n",
            "Epoch 84/140\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4911 - accuracy: 0.7686 - val_loss: 0.4612 - val_accuracy: 0.7869\n",
            "Epoch 85/140\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.5096 - accuracy: 0.7438 - val_loss: 0.7674 - val_accuracy: 0.6393\n",
            "Epoch 86/140\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.6037 - accuracy: 0.6983 - val_loss: 0.6272 - val_accuracy: 0.6721\n",
            "Epoch 87/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5433 - accuracy: 0.7314 - val_loss: 0.4607 - val_accuracy: 0.7869\n",
            "Epoch 88/140\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4652 - accuracy: 0.7810 - val_loss: 0.4641 - val_accuracy: 0.8033\n",
            "Epoch 89/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4639 - accuracy: 0.8017 - val_loss: 0.4689 - val_accuracy: 0.7869\n",
            "Epoch 90/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4653 - accuracy: 0.7893 - val_loss: 0.4503 - val_accuracy: 0.8033\n",
            "Epoch 91/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4644 - accuracy: 0.7851 - val_loss: 0.4769 - val_accuracy: 0.8033\n",
            "Epoch 92/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4562 - accuracy: 0.7727 - val_loss: 0.4658 - val_accuracy: 0.7869\n",
            "Epoch 93/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4614 - accuracy: 0.7975 - val_loss: 0.4360 - val_accuracy: 0.7869\n",
            "Epoch 94/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4822 - accuracy: 0.7645 - val_loss: 0.4485 - val_accuracy: 0.8033\n",
            "Epoch 95/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4718 - accuracy: 0.7727 - val_loss: 0.4759 - val_accuracy: 0.8033\n",
            "Epoch 96/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4643 - accuracy: 0.7851 - val_loss: 0.4608 - val_accuracy: 0.8033\n",
            "Epoch 97/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4624 - accuracy: 0.7934 - val_loss: 0.4574 - val_accuracy: 0.7869\n",
            "Epoch 98/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4528 - accuracy: 0.7975 - val_loss: 0.4734 - val_accuracy: 0.7869\n",
            "Epoch 99/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4648 - accuracy: 0.7727 - val_loss: 0.4649 - val_accuracy: 0.7705\n",
            "Epoch 100/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4547 - accuracy: 0.7769 - val_loss: 0.4750 - val_accuracy: 0.7869\n",
            "Epoch 101/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4495 - accuracy: 0.7686 - val_loss: 0.4464 - val_accuracy: 0.7869\n",
            "Epoch 102/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4598 - accuracy: 0.7810 - val_loss: 0.4435 - val_accuracy: 0.7869\n",
            "Epoch 103/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4979 - accuracy: 0.7645 - val_loss: 0.4493 - val_accuracy: 0.8033\n",
            "Epoch 104/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4839 - accuracy: 0.7645 - val_loss: 0.5266 - val_accuracy: 0.7541\n",
            "Epoch 105/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4609 - accuracy: 0.7934 - val_loss: 0.4701 - val_accuracy: 0.8033\n",
            "Epoch 106/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4764 - accuracy: 0.7603 - val_loss: 0.4959 - val_accuracy: 0.7541\n",
            "Epoch 107/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4751 - accuracy: 0.7603 - val_loss: 0.6546 - val_accuracy: 0.6721\n",
            "Epoch 108/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5401 - accuracy: 0.7314 - val_loss: 0.5775 - val_accuracy: 0.7049\n",
            "Epoch 109/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4680 - accuracy: 0.7686 - val_loss: 0.4491 - val_accuracy: 0.7869\n",
            "Epoch 110/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4494 - accuracy: 0.7810 - val_loss: 0.4465 - val_accuracy: 0.7869\n",
            "Epoch 111/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4518 - accuracy: 0.7851 - val_loss: 0.4439 - val_accuracy: 0.7869\n",
            "Epoch 112/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4790 - accuracy: 0.7603 - val_loss: 0.4452 - val_accuracy: 0.8033\n",
            "Epoch 113/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4478 - accuracy: 0.7975 - val_loss: 0.4399 - val_accuracy: 0.7869\n",
            "Epoch 114/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4441 - accuracy: 0.7975 - val_loss: 0.5219 - val_accuracy: 0.7705\n",
            "Epoch 115/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4512 - accuracy: 0.7769 - val_loss: 0.4476 - val_accuracy: 0.7869\n",
            "Epoch 116/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4451 - accuracy: 0.7975 - val_loss: 0.5902 - val_accuracy: 0.7049\n",
            "Epoch 117/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4818 - accuracy: 0.7727 - val_loss: 0.4649 - val_accuracy: 0.7869\n",
            "Epoch 118/140\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4410 - accuracy: 0.8017 - val_loss: 0.4564 - val_accuracy: 0.7869\n",
            "Epoch 119/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4395 - accuracy: 0.8140 - val_loss: 0.4431 - val_accuracy: 0.8033\n",
            "Epoch 120/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4429 - accuracy: 0.8058 - val_loss: 0.4587 - val_accuracy: 0.7705\n",
            "Epoch 121/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4636 - accuracy: 0.7810 - val_loss: 0.4906 - val_accuracy: 0.7705\n",
            "Epoch 122/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4623 - accuracy: 0.7810 - val_loss: 0.5490 - val_accuracy: 0.7377\n",
            "Epoch 123/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4735 - accuracy: 0.8017 - val_loss: 0.6513 - val_accuracy: 0.6721\n",
            "Epoch 124/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4939 - accuracy: 0.7479 - val_loss: 0.5205 - val_accuracy: 0.7541\n",
            "Epoch 125/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.7686 - val_loss: 0.4515 - val_accuracy: 0.8033\n",
            "Epoch 126/140\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4611 - accuracy: 0.7851 - val_loss: 0.4931 - val_accuracy: 0.7705\n",
            "Epoch 127/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4566 - accuracy: 0.7934 - val_loss: 0.4428 - val_accuracy: 0.7869\n",
            "Epoch 128/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4447 - accuracy: 0.8058 - val_loss: 0.5142 - val_accuracy: 0.7705\n",
            "Epoch 129/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4867 - accuracy: 0.7769 - val_loss: 0.4770 - val_accuracy: 0.7541\n",
            "Epoch 130/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5045 - accuracy: 0.7810 - val_loss: 0.4246 - val_accuracy: 0.8033\n",
            "Epoch 131/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4680 - accuracy: 0.7893 - val_loss: 0.4506 - val_accuracy: 0.7869\n",
            "Epoch 132/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4838 - accuracy: 0.7562 - val_loss: 0.4881 - val_accuracy: 0.7541\n",
            "Epoch 133/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4738 - accuracy: 0.7645 - val_loss: 0.4429 - val_accuracy: 0.8033\n",
            "Epoch 134/140\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4894 - accuracy: 0.7810 - val_loss: 0.4944 - val_accuracy: 0.7705\n",
            "Epoch 135/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4995 - accuracy: 0.7686 - val_loss: 0.5964 - val_accuracy: 0.7213\n",
            "Epoch 136/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4601 - accuracy: 0.7934 - val_loss: 0.4894 - val_accuracy: 0.7869\n",
            "Epoch 137/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4446 - accuracy: 0.7893 - val_loss: 0.4532 - val_accuracy: 0.7705\n",
            "Epoch 138/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4301 - accuracy: 0.8017 - val_loss: 0.4396 - val_accuracy: 0.8033\n",
            "Epoch 139/140\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4478 - accuracy: 0.7603 - val_loss: 0.4470 - val_accuracy: 0.8033\n",
            "Epoch 140/140\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.4648 - accuracy: 0.7727 - val_loss: 0.6061 - val_accuracy: 0.7049\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_train = model.predict(X_train)\n",
        "y_pred_nn_test = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-zlMWuvABDI",
        "outputId": "d0561eb3-8001-4b84-8ec4-594e3dc9ebf1"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_y_train = [round(x[0]) for x in y_pred_nn_train]\n",
        "rounded_y_test= [round(x[0]) for x in y_pred_nn_test]"
      ],
      "metadata": {
        "id": "B1Wn-h_tARA3"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy (train) Neural Network \", accuracy_score(y_train, rounded_y_train))\n",
        "print(\"Accuracy (test) Neural Network \", accuracy_score(y_test, rounded_y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwygSZaVASit",
        "outputId": "751d0093-d16d-49d0-ff5b-ff373a6269da"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (train) Neural Network  0.7479338842975206\n",
            "Accuracy (test) Neural Network  0.7049180327868853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zpEglCRTAYfH",
        "outputId": "8ced8c3c-7632-432a-a978-2740bc8c6ed7"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "HMFOo-FPBO37",
        "outputId": "eb4bf691-ccde-40d0-eb71-d77ac5f323d1"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1pHh41TlBQ-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kesimpulan\n",
        "\n",
        "Berdasarkan kelima percobaan yang telah saya lakukan, dapat saya simpulkan bahwa arsitektur untuk memprediksi seseorang terkena penyakit jantung atau tidak adalah dengan menggunakan **Arsitektur Neural Network CNN**"
      ],
      "metadata": {
        "id": "qvxZ5PFtHwg2"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K9N7GGo6IP1t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}