{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMKHqw2kijXqCdmLtn8I+WY"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Tugas 8 | Studi Kasus Heart Disease 5 Fitur | ANN**"],"metadata":{"id":"_aJGsqUoN-GQ"}},{"cell_type":"markdown","source":["Implementasi dengan menggunakan `ANN`"],"metadata":{"id":"3GF77lV2nWBa"}},{"cell_type":"markdown","source":["## Inisialisasi Model Dan Data ANN"],"metadata":{"id":"xQP1rKfWOI61"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"CVQQqobQGS-Z"},"outputs":[],"source":["import tensorflow as tf\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import keras\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from sklearn.metrics import confusion_matrix"]},{"cell_type":"code","source":["data = pd.read_csv('https://raw.githubusercontent.com/soumya-mishra/Heart-Disease_DT/main/heart_v2.csv')\n","data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"dUsUaIiMGZy1","executionInfo":{"status":"ok","timestamp":1669553813427,"user_tz":-420,"elapsed":5,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"7214bdf7-e535-48d0-ffe9-21a457a13eb8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   age  sex   BP  cholestrol  heart disease\n","0   70    1  130         322              1\n","1   67    0  115         564              0\n","2   57    1  124         261              1\n","3   64    1  128         263              0\n","4   74    0  120         269              0"],"text/html":["\n","  <div id=\"df-9fb024cd-62fd-4381-bbd1-99a0f46a88c4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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<th>3</th>\n","      <td>64</td>\n","      <td>1</td>\n","      <td>128</td>\n","      <td>263</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>74</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>269</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fb024cd-62fd-4381-bbd1-99a0f46a88c4')\"\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 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<td>9.109067</td>\n","      <td>0.468195</td>\n","      <td>17.861608</td>\n","      <td>51.686237</td>\n","      <td>0.497827</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>48.000000</td>\n","      <td>0.000000</td>\n","      <td>120.000000</td>\n","      <td>213.000000</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>130.000000</td>\n","      <td>245.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>1.000000</td>\n","      <td>140.000000</td>\n","      <td>280.000000</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>200.000000</td>\n","      <td>564.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02ceac90-71fc-48de-a51a-b03877b62573')\"\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 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Erha","userId":"13359221303846732984"}},"outputId":"3e0c2016-dcfb-4c33-d2db-8cf05e45cd27"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age              False\n","sex              False\n","BP               False\n","cholestrol       False\n","heart disease    False\n","dtype: bool"]},"metadata":{},"execution_count":73}]},{"cell_type":"code","source":["nama_fitur = data.columns.copy()\n","nama_fitur = nama_fitur.drop('heart disease')\n","nama_fitur"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3tCLlMqMIYF_","executionInfo":{"status":"ok","timestamp":1669553813887,"user_tz":-420,"elapsed":18,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"fea2c385-ab4e-4326-ceca-85c46a9cb770"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['age', 'sex', 'BP', 'cholestrol'], dtype='object')"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","source":["X = data.iloc[:,:4].values\n","y = data[\"heart disease\"].values"],"metadata":{"id":"ViuJpH2-GlAk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H_jyC4BeGuj1","executionInfo":{"status":"ok","timestamp":1669553813888,"user_tz":-420,"elapsed":16,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"bdb02b16-2ee4-4f16-b89c-79ba40615998"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 70,   1, 130, 322],\n","       [ 67,   0, 115, 564],\n","       [ 57,   1, 124, 261],\n","       ...,\n","       [ 56,   0, 140, 294],\n","       [ 57,   1, 140, 192],\n","       [ 67,   1, 160, 286]])"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k7-G675OGvoN","executionInfo":{"status":"ok","timestamp":1669553813889,"user_tz":-420,"elapsed":16,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"8843bbd3-9942-4b2f-d37e-23573f93a1f3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n","       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n","       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n","       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n","       0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n","       1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n","       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n","       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n","       1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n","       0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n","       1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n","       0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n","       1, 0, 0, 0, 0, 1])"]},"metadata":{},"execution_count":77}]},{"cell_type":"markdown","source":["### Preprosessing Data"],"metadata":{"id":"RM5kB2b8OO7a"}},{"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","scaler = MinMaxScaler()\n","scaled = scaler.fit_transform(X)\n","scaled_fitur = pd.DataFrame(scaled,columns=nama_fitur)\n","scaled_fitur"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"owWugtmiJf-h","executionInfo":{"status":"ok","timestamp":1669553813889,"user_tz":-420,"elapsed":15,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"3418d222-61ee-4446-9998-cbc373e871ef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          age  sex        BP  cholestrol\n","0    0.854167  1.0  0.339623    0.447489\n","1    0.791667  0.0  0.198113    1.000000\n","2    0.583333  1.0  0.283019    0.308219\n","3    0.729167  1.0  0.320755    0.312785\n","4    0.937500  0.0  0.245283    0.326484\n","..        ...  ...       ...         ...\n","265  0.479167  1.0  0.735849    0.166667\n","266  0.312500  1.0  0.245283    0.312785\n","267  0.562500  0.0  0.433962    0.383562\n","268  0.583333  1.0  0.433962    0.150685\n","269  0.791667  1.0  0.622642    0.365297\n","\n","[270 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-dc35cc68-cded-496c-9f57-daf2e0763380\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    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<td>0.320755</td>\n","      <td>0.312785</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.937500</td>\n","      <td>0.0</td>\n","      <td>0.245283</td>\n","      <td>0.326484</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>0.479167</td>\n","      <td>1.0</td>\n","      <td>0.735849</td>\n","      <td>0.166667</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>0.312500</td>\n","      <td>1.0</td>\n","      <td>0.245283</td>\n","      <td>0.312785</td>\n","    </tr>\n","    <tr>\n","      <th>267</th>\n","      <td>0.562500</td>\n","      <td>0.0</td>\n","      <td>0.433962</td>\n","      <td>0.383562</td>\n","    </tr>\n","    <tr>\n","      <th>268</th>\n","      <td>0.583333</td>\n","      <td>1.0</td>\n","      <td>0.433962</td>\n","      <td>0.150685</td>\n","    </tr>\n","    <tr>\n","      <th>269</th>\n","      <td>0.791667</td>\n","      <td>1.0</td>\n","      <td>0.622642</td>\n","      <td>0.365297</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>270 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc35cc68-cded-496c-9f57-daf2e0763380')\"\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 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0s 2ms/step - loss: 0.6930 - accuracy: 0.5324\n","Epoch 2/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6924 - accuracy: 0.5509\n","Epoch 3/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6920 - accuracy: 0.5509\n","Epoch 4/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6915 - accuracy: 0.5509\n","Epoch 5/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6911 - accuracy: 0.5509\n","Epoch 6/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6908 - accuracy: 0.5509\n","Epoch 7/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6899 - accuracy: 0.5509\n","Epoch 8/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6892 - accuracy: 0.5509\n","Epoch 9/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6885 - accuracy: 0.5509\n","Epoch 10/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6874 - accuracy: 0.5509\n","Epoch 11/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6863 - accuracy: 0.5509\n","Epoch 12/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6847 - accuracy: 0.5509\n","Epoch 13/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6825 - accuracy: 0.5509\n","Epoch 14/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6800 - accuracy: 0.5509\n","Epoch 15/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6781 - accuracy: 0.5509\n","Epoch 16/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6734 - accuracy: 0.5509\n","Epoch 17/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6699 - accuracy: 0.5509\n","Epoch 18/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6657 - accuracy: 0.5509\n","Epoch 19/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6634 - accuracy: 0.5509\n","Epoch 20/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6589 - accuracy: 0.5741\n","Epoch 21/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6555 - accuracy: 0.6111\n","Epoch 22/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6514 - accuracy: 0.6204\n","Epoch 23/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6488 - accuracy: 0.6435\n","Epoch 24/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6470 - accuracy: 0.6343\n","Epoch 25/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6446 - accuracy: 0.6250\n","Epoch 26/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6432 - accuracy: 0.6389\n","Epoch 27/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6414 - accuracy: 0.6250\n","Epoch 28/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6398 - accuracy: 0.6343\n","Epoch 29/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6386 - accuracy: 0.6250\n","Epoch 30/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6368 - accuracy: 0.6389\n","Epoch 31/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6367 - accuracy: 0.6389\n","Epoch 32/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6348 - accuracy: 0.6250\n","Epoch 33/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6347 - accuracy: 0.6250\n","Epoch 34/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6334 - accuracy: 0.6296\n","Epoch 35/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6320 - accuracy: 0.6343\n","Epoch 36/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6308 - accuracy: 0.6250\n","Epoch 37/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6292 - accuracy: 0.6296\n","Epoch 38/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6284 - accuracy: 0.6481\n","Epoch 39/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6283 - accuracy: 0.6250\n","Epoch 40/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6276 - accuracy: 0.6389\n","Epoch 41/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6264 - accuracy: 0.6250\n","Epoch 42/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6243 - accuracy: 0.6389\n","Epoch 43/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6246 - accuracy: 0.6204\n","Epoch 44/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6230 - accuracy: 0.6250\n","Epoch 45/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6224 - accuracy: 0.6481\n","Epoch 46/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6208 - accuracy: 0.6389\n","Epoch 47/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6204 - accuracy: 0.6343\n","Epoch 48/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6200 - accuracy: 0.6343\n","Epoch 49/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6189 - accuracy: 0.6343\n","Epoch 50/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6174 - accuracy: 0.6435\n","Epoch 51/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6171 - accuracy: 0.6296\n","Epoch 52/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6156 - accuracy: 0.6389\n","Epoch 53/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6157 - accuracy: 0.6435\n","Epoch 54/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6141 - accuracy: 0.6481\n","Epoch 55/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6139 - accuracy: 0.6343\n","Epoch 56/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6125 - accuracy: 0.6481\n","Epoch 57/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6123 - accuracy: 0.6481\n","Epoch 58/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6113 - accuracy: 0.6435\n","Epoch 59/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6123 - accuracy: 0.6343\n","Epoch 60/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6093 - accuracy: 0.6574\n","Epoch 61/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6101 - accuracy: 0.6481\n","Epoch 62/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6086 - accuracy: 0.6435\n","Epoch 63/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6072 - accuracy: 0.6528\n","Epoch 64/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6079 - accuracy: 0.6481\n","Epoch 65/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6065 - accuracy: 0.6574\n","Epoch 66/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6060 - accuracy: 0.6435\n","Epoch 67/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6078 - accuracy: 0.6528\n","Epoch 68/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6046 - accuracy: 0.6481\n","Epoch 69/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6042 - accuracy: 0.6481\n","Epoch 70/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6047 - accuracy: 0.6574\n","Epoch 71/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6038 - accuracy: 0.6528\n","Epoch 72/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6035 - accuracy: 0.6435\n","Epoch 73/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6044 - accuracy: 0.6667\n","Epoch 74/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6033 - accuracy: 0.6528\n","Epoch 75/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6020 - accuracy: 0.6528\n","Epoch 76/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6026 - accuracy: 0.6620\n","Epoch 77/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6002 - accuracy: 0.6574\n","Epoch 78/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6022 - accuracy: 0.6528\n","Epoch 79/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5994 - accuracy: 0.6667\n","Epoch 80/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5997 - accuracy: 0.6759\n","Epoch 81/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5982 - accuracy: 0.6620\n","Epoch 82/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5985 - accuracy: 0.6667\n","Epoch 83/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5988 - accuracy: 0.6620\n","Epoch 84/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5982 - accuracy: 0.6713\n","Epoch 85/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5973 - accuracy: 0.6574\n","Epoch 86/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5973 - accuracy: 0.6574\n","Epoch 87/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5972 - accuracy: 0.6713\n","Epoch 88/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5977 - accuracy: 0.6667\n","Epoch 89/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5972 - accuracy: 0.6667\n","Epoch 90/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5963 - accuracy: 0.6759\n","Epoch 91/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5973 - accuracy: 0.6574\n","Epoch 92/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5977 - accuracy: 0.6574\n","Epoch 93/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5963 - accuracy: 0.6528\n","Epoch 94/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5952 - accuracy: 0.6667\n","Epoch 95/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.6000 - accuracy: 0.6713\n","Epoch 96/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5946 - accuracy: 0.6667\n","Epoch 97/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5980 - accuracy: 0.6481\n","Epoch 98/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5950 - accuracy: 0.6574\n","Epoch 99/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5947 - accuracy: 0.6620\n","Epoch 100/100\n","27/27 [==============================] - 0s 2ms/step - loss: 0.5942 - accuracy: 0.6574\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fdbc68b02d0>"]},"metadata":{},"execution_count":113}]},{"cell_type":"markdown","source":["### Predict data With X_Test"],"metadata":{"id":"HbXw7QdIOeG5"}},{"cell_type":"code","source":["\n","y_pred = classifier.predict(X_test)\n","y_pred = (y_pred > 0.5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cTB6qOt_Ny7i","executionInfo":{"status":"ok","timestamp":1669554715073,"user_tz":-420,"elapsed":3,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"22ebb667-8e05-44de-c2c4-164a7c91db7a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2/2 [==============================] - 0s 5ms/step\n"]}]},{"cell_type":"code","source":["cm = confusion_matrix(y_test,y_pred)\n","cm"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UsO7g4OdN2yD","executionInfo":{"status":"ok","timestamp":1669554729586,"user_tz":-420,"elapsed":334,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"5e38f344-19c2-4b72-82e2-ccc6b53d1b96"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[21, 10],\n","       [ 6, 17]])"]},"metadata":{},"execution_count":115}]},{"cell_type":"markdown","source":["### Akurasi ANN"],"metadata":{"id":"wgFOgflVOisX"}},{"cell_type":"code","source":["\n","accuracy = (cm[0][0]+cm[1][1])/(cm[0][1] + cm[1][0] +cm[0][0] +cm[1][1])\n","print(accuracy*100)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WAlVolP7N6uC","executionInfo":{"status":"ok","timestamp":1669554745784,"user_tz":-420,"elapsed":304,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"2d2a52f1-deab-428c-cc95-e5e87d2ddef6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["70.37037037037037\n"]}]}]}