{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":29653,"databundleVersionId":2420395,"sourceType":"competition"}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport torch\nimport torch.nn as nn\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pydicom as dicom\nimport cv2\nimport ast\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.801326Z","iopub.execute_input":"2026-07-10T20:10:27.802021Z","iopub.status.idle":"2026-07-10T20:10:27.806691Z","shell.execute_reply.started":"2026-07-10T20:10:27.801984Z","shell.execute_reply":"2026-07-10T20:10:27.805949Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/'\nos.listdir(path)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.815055Z","iopub.execute_input":"2026-07-10T20:10:27.815393Z","iopub.status.idle":"2026-07-10T20:10:27.830299Z","shell.execute_reply.started":"2026-07-10T20:10:27.815374Z","shell.execute_reply":"2026-07-10T20:10:27.829324Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/'\nos.listdir(path)\ntrain_data = pd.read_csv(path+'train_labels.csv')\nsamp_subm = pd.read_csv(path+'sample_submission.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.831647Z","iopub.execute_input":"2026-07-10T20:10:27.831839Z","iopub.status.idle":"2026-07-10T20:10:27.852177Z","shell.execute_reply.started":"2026-07-10T20:10:27.831827Z","shell.execute_reply":"2026-07-10T20:10:27.851248Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Samples train:', len(train_data))\nprint('Samples test:', len(samp_subm))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.853212Z","iopub.execute_input":"2026-07-10T20:10:27.853438Z","iopub.status.idle":"2026-07-10T20:10:27.862048Z","shell.execute_reply.started":"2026-07-10T20:10:27.853423Z","shell.execute_reply":"2026-07-10T20:10:27.860924Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.863932Z","iopub.execute_input":"2026-07-10T20:10:27.864207Z","iopub.status.idle":"2026-07-10T20:10:27.886599Z","shell.execute_reply.started":"2026-07-10T20:10:27.86419Z","shell.execute_reply":"2026-07-10T20:10:27.885301Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_data[\"MGMT_value\"].value_counts().head(2).plot(kind = 'pie', autopct='%1.1f%%', figsize=(8, 8)).legend()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:27.887862Z","iopub.execute_input":"2026-07-10T20:10:27.88816Z","iopub.status.idle":"2026-07-10T20:10:28.006902Z","shell.execute_reply.started":"2026-07-10T20:10:27.888135Z","shell.execute_reply":"2026-07-10T20:10:28.006061Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_data[\"MGMT_value\"].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.007766Z","iopub.execute_input":"2026-07-10T20:10:28.00802Z","iopub.status.idle":"2026-07-10T20:10:28.015185Z","shell.execute_reply.started":"2026-07-10T20:10:28.007997Z","shell.execute_reply":"2026-07-10T20:10:28.014534Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"samp_subm.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.015853Z","iopub.execute_input":"2026-07-10T20:10:28.016054Z","iopub.status.idle":"2026-07-10T20:10:28.042027Z","shell.execute_reply.started":"2026-07-10T20:10:28.016036Z","shell.execute_reply":"2026-07-10T20:10:28.040675Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"folder = str(train_data.loc[0, 'BraTS21ID']).zfill(5)\nfolder","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.043042Z","iopub.execute_input":"2026-07-10T20:10:28.043306Z","iopub.status.idle":"2026-07-10T20:10:28.0656Z","shell.execute_reply.started":"2026-07-10T20:10:28.043287Z","shell.execute_reply":"2026-07-10T20:10:28.064368Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"os.listdir(path+'train/'+folder)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.06684Z","iopub.execute_input":"2026-07-10T20:10:28.067086Z","iopub.status.idle":"2026-07-10T20:10:28.095385Z","shell.execute_reply.started":"2026-07-10T20:10:28.067066Z","shell.execute_reply":"2026-07-10T20:10:28.094284Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Number of FLAIR images:', len(os.listdir(path+'train/'+folder+'/'+'FLAIR')))\nprint('Number of T1w images:', len(os.listdir(path+'train/'+folder+'/'+'T1w')))\nprint('Number of T1wCE images:', len(os.listdir(path+'train/'+folder+'/'+'T1wCE')))\nprint('Number of T2w images:', len(os.listdir(path+'train/'+folder+'/'+'T2w')))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.098212Z","iopub.execute_input":"2026-07-10T20:10:28.098454Z","iopub.status.idle":"2026-07-10T20:10:28.109754Z","shell.execute_reply.started":"2026-07-10T20:10:28.098435Z","shell.execute_reply":"2026-07-10T20:10:28.108466Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path_file = ''.join([path, 'train/', folder, '/', 'FLAIR/'])\nimage = os.listdir(path_file)[0]\ndata_file = dicom.dcmread(path_file+image)\nimg = data_file.pixel_array\nprint('Image shape:', img.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.110785Z","iopub.execute_input":"2026-07-10T20:10:28.111061Z","iopub.status.idle":"2026-07-10T20:10:28.133926Z","shell.execute_reply.started":"2026-07-10T20:10:28.111036Z","shell.execute_reply":"2026-07-10T20:10:28.132521Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Flair Image\ndef plot_examples(row = 0, cat = 'FLAIR'): \n    folder = str(train_data.loc[row, 'BraTS21ID']).zfill(5)\n    path_file = ''.join([path, 'train/', folder, '/', cat, '/'])\n    images = os.listdir(path_file)\n    \n    fig, axs = plt.subplots(1, 5, figsize=(30, 30))\n    fig.subplots_adjust(hspace = .2, wspace=.2)\n    axs = axs.ravel()\n    \n    for num in range(5):\n        data_file = dicom.dcmread(path_file+images[num])\n        img = data_file.pixel_array\n        axs[num].imshow(img, cmap='gray')\n        axs[num].set_title(cat+' '+images[num])\n        axs[num].set_xticklabels([])\n        axs[num].set_yticklabels([])\n        \nrow = 0\nplot_examples(row = row, cat = 'FLAIR')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.135201Z","iopub.execute_input":"2026-07-10T20:10:28.135474Z","iopub.status.idle":"2026-07-10T20:10:28.716873Z","shell.execute_reply.started":"2026-07-10T20:10:28.135454Z","shell.execute_reply":"2026-07-10T20:10:28.715713Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#T1w Images\nplot_examples(row = row, cat = 'T1w')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:28.717835Z","iopub.execute_input":"2026-07-10T20:10:28.71809Z","iopub.status.idle":"2026-07-10T20:10:29.268509Z","shell.execute_reply.started":"2026-07-10T20:10:28.718069Z","shell.execute_reply":"2026-07-10T20:10:29.267403Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#T1wCE Images\nplot_examples(row = row, cat = 'T1wCE')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:29.269518Z","iopub.execute_input":"2026-07-10T20:10:29.269808Z","iopub.status.idle":"2026-07-10T20:10:29.814871Z","shell.execute_reply.started":"2026-07-10T20:10:29.269785Z","shell.execute_reply":"2026-07-10T20:10:29.813849Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#T2w Images\nplot_examples(row = row, cat = 'T2w')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:29.815731Z","iopub.execute_input":"2026-07-10T20:10:29.815953Z","iopub.status.idle":"2026-07-10T20:10:30.393181Z","shell.execute_reply.started":"2026-07-10T20:10:29.815936Z","shell.execute_reply":"2026-07-10T20:10:30.392244Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport pydicom\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nfrom sklearn.model_selection import train_test_split\nimport cv2\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n\n# Path dataset\npath = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/'\ntrain_labels = pd.read_csv(path + 'train_labels.csv')\n\n# Konfigurasi\nIMG_SIZE = 128\nBATCH_SIZE = 32\nEPOCHS = 15\nMODALITY = 'FLAIR' \n\n# Fungsi untuk membaca dan memproses gambar DICOM\ndef load_dicom_image(filepath, img_size=IMG_SIZE):\n    dicom = pydicom.dcmread(filepath)\n    img = dicom.pixel_array.astype(float)\n    \n    # Normalisasi\n    img = (img - img.min()) / (img.max() - img.min())\n    \n    # Konversi ke uint8\n    img = (img * 255).astype(np.uint8)\n    \n    # Resize\n    img = cv2.resize(img, (img_size, img_size))\n    \n    # Stack ke 3 channel\n    img = np.stack([img]*3, axis=-1)\n    return img\n\n# Fungsi untuk memuat data pasien\ndef load_patient_data(patient_id, num_slices=16):\n    patient_path = os.path.join(path, 'train', str(patient_id).zfill(5), MODALITY)\n    slices = []\n    \n    if not os.path.exists(patient_path):\n        print(f\"Data tidak ditemukan untuk pasien {patient_id}\")\n        return None\n    \n    # Dapatkan semua file DICOM\n    dicom_files = sorted([f for f in os.listdir(patient_path) if f.endswith('.dcm')])\n    \n    if not dicom_files:\n        print(f\"Tidak ada file DICOM untuk pasien {patient_id}\")\n        return None\n    \n    # Pilih slice secara merata\n    step = max(1, len(dicom_files) // num_slices)\n    selected_files = dicom_files[::step][:num_slices]\n    \n    # Muat slice yang dipilih\n    for filename in selected_files:\n        img_path = os.path.join(patient_path, filename)\n        img = load_dicom_image(img_path)\n        slices.append(img)\n    \n    # Jika tidak cukup slice, duplikat yang terakhir\n    while len(slices) < num_slices:\n        slices.append(slices[-1].copy())  # Gunakan copy untuk menghindari reference yang sama\n    \n    return np.array(slices)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:30.394022Z","iopub.execute_input":"2026-07-10T20:10:30.394278Z","iopub.status.idle":"2026-07-10T20:10:30.406949Z","shell.execute_reply.started":"2026-07-10T20:10:30.394261Z","shell.execute_reply":"2026-07-10T20:10:30.405917Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Membuat dataset\nX = []\ny = []\n\nprint(\"Memuat data training...\")\nfor idx, row in train_labels.iterrows():\n    patient_id = row['BraTS21ID']\n    label = row['MGMT_value']\n    \n    patient_data = load_patient_data(patient_id)\n    if patient_data is not None:\n        X.append(patient_data)\n        y.append(label)\n\n# Konversi ke numpy array\nX = np.array(X, dtype=np.float32)\ny = np.array(y, dtype=np.float32)\n\nprint(f\"Total data yang dimuat: {len(X)} sampel\")\nprint(f\"Distribusi kelas: {np.sum(y == 1)} positif, {np.sum(y == 0)} negatif\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:10:30.408992Z","iopub.execute_input":"2026-07-10T20:10:30.409272Z","iopub.status.idle":"2026-07-10T20:11:38.150385Z","shell.execute_reply.started":"2026-07-10T20:10:30.409252Z","shell.execute_reply":"2026-07-10T20:11:38.149148Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Split data: training (60%), validation (20%), test (20%)\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X, y, test_size=0.4, random_state=42, stratify=y\n)\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp\n)\n\nprint(\"\\nDistribusi dataset:\")\nprint(f\"Training:   {len(X_train)} sampel\")\nprint(f\"Validation: {len(X_val)} sampel\")\nprint(f\"Test:       {len(X_test)} sampel\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:38.151721Z","iopub.execute_input":"2026-07-10T20:11:38.152046Z","iopub.status.idle":"2026-07-10T20:11:39.507603Z","shell.execute_reply.started":"2026-07-10T20:11:38.152023Z","shell.execute_reply":"2026-07-10T20:11:39.506446Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def augment(img):\n    if np.random.rand() > 0.5:\n        img = img[:, ::-1, :]  # flip horizontal\n    if np.random.rand() > 0.5:\n        img = np.clip(img * np.random.uniform(0.9, 1.1), 0, 255)  # contraste\n    return img\n\nX_aug = np.array([[augment(s) for s in vol] for vol in X_train])\nX_train2 = np.concatenate([X_train, X_aug])\ny_train2 = np.concatenate([y_train, y_train])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:39.508831Z","iopub.execute_input":"2026-07-10T20:11:39.509019Z","iopub.status.idle":"2026-07-10T20:11:43.247928Z","shell.execute_reply.started":"2026-07-10T20:11:39.509005Z","shell.execute_reply":"2026-07-10T20:11:43.246788Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def build_2d_cnn_lstm(input_shape, num_classes):\n    inputs = layers.Input(shape=input_shape)\n    \n    x = layers.TimeDistributed(layers.Conv2D(16, (3,3), activation='relu', padding='same'))(inputs)\n    x = layers.TimeDistributed(layers.MaxPooling2D((2,2)))(x)\n    x = layers.TimeDistributed(layers.Conv2D(32, (3,3), activation='relu', padding='same'))(x)\n    x = layers.TimeDistributed(layers.MaxPooling2D((2,2)))(x)\n    x = layers.TimeDistributed(layers.GlobalAveragePooling2D())(x)  # (16, 32)\n    \n    x = layers.LSTM(32)(x)  # agrège la séquence des 16 coupes\n    \n    x = layers.Dense(32, activation='relu')(x)\n    x = layers.Dropout(0.5)(x)\n    outputs = layers.Dense(num_classes, activation='sigmoid')(x)\n    \n    return models.Model(inputs, outputs)\n\nmodel = build_2d_cnn_lstm(input_shape, num_classes=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:43.249275Z","iopub.execute_input":"2026-07-10T20:11:43.249477Z","iopub.status.idle":"2026-07-10T20:11:43.309983Z","shell.execute_reply.started":"2026-07-10T20:11:43.249461Z","shell.execute_reply":"2026-07-10T20:11:43.30891Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Bangun model\ninput_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3], X_train.shape[4])\nprint(f\"\\nInput shape: {input_shape}\")\nmodel = build_3d_cnn(input_shape, num_classes=1)\n\n# Ringkasan model\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:43.310892Z","iopub.execute_input":"2026-07-10T20:11:43.311093Z","iopub.status.idle":"2026-07-10T20:11:43.398374Z","shell.execute_reply.started":"2026-07-10T20:11:43.311077Z","shell.execute_reply":"2026-07-10T20:11:43.397248Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Kompilasi model\nmodel = build_2d_cnn_lstm(input_shape, num_classes=1)\nmodel.compile(\n    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n    loss='binary_crossentropy',\n    metrics=['accuracy', tf.keras.metrics.AUC(name='auc')]\n)\n\n# Callback\ncallbacks = [\n    tf.keras.callbacks.EarlyStopping(\n        patience=5, \n        monitor='val_auc', \n        mode='max', \n        restore_best_weights=True,\n        verbose=1\n    ),\n    tf.keras.callbacks.ReduceLROnPlateau(\n        monitor='val_loss', \n        factor=0.2, \n        patience=3, \n        min_lr=1e-6,\n        verbose=1\n    ),\n    tf.keras.callbacks.ModelCheckpoint(\n        filepath='best_model.h5',\n        save_best_only=True,\n        monitor='val_auc',\n        mode='max',\n        verbose=1\n    )\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:43.39919Z","iopub.execute_input":"2026-07-10T20:11:43.399384Z","iopub.status.idle":"2026-07-10T20:11:43.462243Z","shell.execute_reply.started":"2026-07-10T20:11:43.399369Z","shell.execute_reply":"2026-07-10T20:11:43.461321Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Training\nprint(\"\\nMemulai training...\")\nhistory = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    batch_size=BATCH_SIZE,\n    epochs=EPOCHS,\n    callbacks=callbacks\n)\n\n# Plot history training\ndef plot_history(history):\n    plt.figure(figsize=(12, 5))\n    \n    # Plot loss\n    plt.subplot(1, 2, 1)\n    plt.plot(history.history['loss'], label='Training Loss')\n    plt.plot(history.history['val_loss'], label='Validation Loss')\n    plt.title('Training and Validation Loss')\n    plt.xlabel('Epoch')\n    plt.ylabel('Loss')\n    plt.legend()\n    \n    # Plot AUC\n    plt.subplot(1, 2, 2)\n    plt.plot(history.history['auc'], label='Training AUC')\n    plt.plot(history.history['val_auc'], label='Validation AUC')\n    plt.title('Training and Validation AUC')\n    plt.xlabel('Epoch')\n    plt.ylabel('AUC')\n    plt.legend()\n    \n    plt.tight_layout()\n    plt.savefig('training_history.png')\n    plt.show()\n\nplot_history(history)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:11:43.463246Z","iopub.execute_input":"2026-07-10T20:11:43.463458Z","iopub.status.idle":"2026-07-10T20:33:33.251247Z","shell.execute_reply.started":"2026-07-10T20:11:43.463443Z","shell.execute_reply":"2026-07-10T20:33:33.250109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Evaluasi pada validation set\nprint(\"\\nEvaluasi pada validation set:\")\nval_loss, val_acc, val_auc = model.evaluate(X_val, y_val)\nprint(f\"Validation Loss: {val_loss:.4f}\")\nprint(f\"Validation Accuracy: {val_acc:.4f}\")\nprint(f\"Validation AUC: {val_auc:.4f}\")\n\n# Evaluasi pada test set\nprint(\"\\nEvaluasi pada test set:\")\ntest_loss, test_acc, test_auc = model.evaluate(X_test, y_test)\nprint(f\"Test Loss: {test_loss:.4f}\")\nprint(f\"Test Accuracy: {test_acc:.4f}\")\nprint(f\"Test AUC: {test_auc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:33.252665Z","iopub.execute_input":"2026-07-10T20:33:33.252857Z","iopub.status.idle":"2026-07-10T20:33:40.655096Z","shell.execute_reply.started":"2026-07-10T20:33:33.25284Z","shell.execute_reply":"2026-07-10T20:33:40.653862Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Prediksi pada test set\ny_pred_prob = model.predict(X_test).flatten()\ny_pred = (y_pred_prob > 0.5).astype(int)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:40.656459Z","iopub.execute_input":"2026-07-10T20:33:40.656749Z","iopub.status.idle":"2026-07-10T20:33:45.156461Z","shell.execute_reply.started":"2026-07-10T20:33:40.656733Z","shell.execute_reply":"2026-07-10T20:33:45.155539Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Classification report\nprint(\"\\nClassification Report:\")\nprint(classification_report(y_test, y_pred))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:45.157712Z","iopub.execute_input":"2026-07-10T20:33:45.157997Z","iopub.status.idle":"2026-07-10T20:33:45.172183Z","shell.execute_reply.started":"2026-07-10T20:33:45.157973Z","shell.execute_reply":"2026-07-10T20:33:45.171233Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Confusion matrix\nconf_matrix = confusion_matrix(y_test, y_pred)\nprint(\"\\nConfusion Matrix:\")\nprint(conf_matrix)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:45.173339Z","iopub.execute_input":"2026-07-10T20:33:45.173626Z","iopub.status.idle":"2026-07-10T20:33:45.19323Z","shell.execute_reply.started":"2026-07-10T20:33:45.173604Z","shell.execute_reply":"2026-07-10T20:33:45.1923Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Plot ROC curve\nfpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\nroc_auc = auc(fpr, tpr)\n\nplt.figure()\nplt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic')\nplt.legend(loc=\"lower right\")\nplt.savefig('roc_curve.png')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:45.197396Z","iopub.execute_input":"2026-07-10T20:33:45.197635Z","iopub.status.idle":"2026-07-10T20:33:45.408208Z","shell.execute_reply.started":"2026-07-10T20:33:45.19762Z","shell.execute_reply":"2026-07-10T20:33:45.407268Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Simpan model akhir\nmodel.save('final_model.h5')\nprint(\"\\nModel akhir disimpan sebagai 'final_model.h5'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-10T20:33:45.412307Z","iopub.execute_input":"2026-07-10T20:33:45.412515Z","iopub.status.idle":"2026-07-10T20:33:45.446328Z","shell.execute_reply.started":"2026-07-10T20:33:45.412477Z","shell.execute_reply":"2026-07-10T20:33:45.44555Z"}},"outputs":[],"execution_count":null}]}