finally

mobile_TA/padi_app/lib/screen/history_screen.dart

@@ -214,7 +214,7 @@ class _HistoryScreenState extends State<HistoryScreen> {
                                   ),
                                   _infoRow(
                                     icon: Icons.bar_chart,
-                                    text: 'Confidence: ${item.confidence}',
+                                    text: 'Akurasi: ${item.confidence}',
                                   ),
                                   _infoRow(
                                     icon: Icons.location_on,

mobile_TA/padi_app/lib/screen/result_screen.dart

@@ -72,7 +72,7 @@ class _ResultScreenState extends State<ResultScreen> {
                     child: Column(
                       children: [
                         Text(
-                          "Confidence: ${widget.result.confidence}",
+                          "Akurasi: ${widget.result.confidence}",
                           style: TextStyle(
                             color: Colors.green[700],
                             fontWeight: FontWeight.bold,

rice leaf diseases dataset/rice_leaf_cnn_classification.ipynb

@@ -2102,6 +2102,14 @@
     "print(\"-\" * 50)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eef67333-43df-44eb-a7e2-9d6bc9e74eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -2162,6 +2170,40 @@
     "print(\"✓ Data augmentation configured!\\n\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0940e186-9b40-4ad3-b578-9b7012f50b99",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'train_generator' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Pastikan 'train_generator' adalah objek yang Anda buat dengan .flow_from_directory()\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;66;03m# Ambil satu batch gambar\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m images, labels = \u001b[38;5;28mnext\u001b[39m(\u001b[43mtrain_generator\u001b[49m)\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Tampilkan 5 gambar pertama dari batch tersebut\u001b[39;00m\n\u001b[32m      6\u001b[39m plt.figure(figsize=(\u001b[32m15\u001b[39m, \u001b[32m5\u001b[39m))\n",
+      "\u001b[31mNameError\u001b[39m: name 'train_generator' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Pastikan 'train_generator' adalah objek yang Anda buat dengan .flow_from_directory()\n",
+    "# Ambil satu batch gambar\n",
+    "images, labels = next(train_generator)\n",
+    "\n",
+    "# Tampilkan 5 gambar pertama dari batch tersebut\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "for i in range(5):\n",
+    "    plt.subplot(1, 5, i+1)\n",
+    "    # ImageDataGenerator biasanya menghasilkan data dalam rentang [0, 255] atau [0, 1]\n",
+    "    # Jika data sudah dinormalisasi (rescale 1./255), Anda mungkin perlu mengalikannya kembali agar terlihat normal\n",
+    "    plt.imshow(images[i]) \n",
+    "    plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "99a5ed48",