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.ipynb_checkpoints/leaf-classification-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import libraries\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, models\n",
+    "from matplotlib import pyplot as plt\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1674 images belonging to 8 classes.\n",
+      "Found 157 images belonging to 8 classes.\n",
+      "Found 79 images belonging to 8 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "TRAIN_DIR = 'dataset/train'\n",
+    "TEST_DIR = 'dataset/test'\n",
+    "VAL_DIR = 'dataset/val'\n",
+    "# Load dataset\n",
+    "datagen = ImageDataGenerator(rescale=1./255)\n",
+    "# Load data dari direktori menggunakan flow_from_directory\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "    TRAIN_DIR,\n",
+    "    target_size=(224, 224),  # Sesuaikan dengan ukuran gambar input model\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "val_generator = datagen.flow_from_directory(\n",
+    "    VAL_DIR,\n",
+    "    target_size=(224, 224),\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "test_generator = datagen.flow_from_directory(\n",
+    "    TEST_DIR,\n",
+    "    target_size=(224, 224),\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    "    shuffle=False  # Untuk testing, tidak perlu shuffle\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Daun Jambu Biji': 0,\n",
+       " 'Daun Kemangi': 1,\n",
+       " 'Daun Kunyit': 2,\n",
+       " 'Daun Mint': 3,\n",
+       " 'Daun Pepaya': 4,\n",
+       " 'Daun Sirih': 5,\n",
+       " 'Daun Sirsak': 6,\n",
+       " 'Lidah Buaya': 7}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_generator.class_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = models.Sequential()\n",
+    "\n",
+    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "\n",
+    "model.add(layers.Flatten())\n",
+    "model.add(layers.Dense(512, activation='relu'))\n",
+    "model.add(layers.Dense(8, activation='softmax'))\n",
+    "\n",
+    "model.compile(optimizer='adam',\n",
+    "                loss='categorical_crossentropy',\n",
+    "                metrics=['accuracy'])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18432</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">9,437,696</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,104</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m18,496\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼───────────────��────────┼───────────────┤\n",
+       "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18432\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │     \u001b[38;5;34m9,437,696\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │         \u001b[38;5;34m4,104\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,682,632</span> (36.94 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,682,632\u001b[0m (36.94 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,682,632</span> (36.94 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,682,632\u001b[0m (36.94 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-14 11:16:05.231584: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n",
+      "/Users/edoaurahman/development/anaconda/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 353ms/step - accuracy: 0.2478 - loss: 2.1296 - val_accuracy: 0.5987 - val_loss: 1.1171\n",
+      "Epoch 2/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 339ms/step - accuracy: 0.6152 - loss: 1.0357 - val_accuracy: 0.6688 - val_loss: 0.8430\n",
+      "Epoch 3/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 322ms/step - accuracy: 0.7471 - loss: 0.7063 - val_accuracy: 0.7898 - val_loss: 0.6230\n",
+      "Epoch 4/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 370ms/step - accuracy: 0.8481 - loss: 0.4345 - val_accuracy: 0.8408 - val_loss: 0.5627\n",
+      "Epoch 5/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 471ms/step - accuracy: 0.9096 - loss: 0.2562 - val_accuracy: 0.8408 - val_loss: 0.5344\n",
+      "Epoch 6/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 363ms/step - accuracy: 0.9161 - loss: 0.2274 - val_accuracy: 0.8408 - val_loss: 0.8011\n",
+      "Epoch 7/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 367ms/step - accuracy: 0.9671 - loss: 0.0961 - val_accuracy: 0.8408 - val_loss: 0.6227\n",
+      "Epoch 8/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 403ms/step - accuracy: 0.9832 - loss: 0.0657 - val_accuracy: 0.7898 - val_loss: 0.9990\n",
+      "Epoch 9/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 420ms/step - accuracy: 0.9750 - loss: 0.0758 - val_accuracy: 0.8344 - val_loss: 0.8001\n",
+      "Epoch 10/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 378ms/step - accuracy: 0.9909 - loss: 0.0312 - val_accuracy: 0.8344 - val_loss: 1.0499\n",
+      "Epoch 11/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 359ms/step - accuracy: 0.9803 - loss: 0.0627 - val_accuracy: 0.8599 - val_loss: 0.8847\n",
+      "Epoch 12/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 369ms/step - accuracy: 0.9984 - loss: 0.0089 - val_accuracy: 0.8280 - val_loss: 1.0634\n",
+      "Epoch 13/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 377ms/step - accuracy: 0.9980 - loss: 0.0106 - val_accuracy: 0.8217 - val_loss: 1.2077\n",
+      "Epoch 14/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 333ms/step - accuracy: 0.9768 - loss: 0.0614 - val_accuracy: 0.8535 - val_loss: 0.8965\n",
+      "Epoch 15/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 345ms/step - accuracy: 0.9867 - loss: 0.0368 - val_accuracy: 0.7962 - val_loss: 1.3721\n",
+      "Epoch 16/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━��━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 408ms/step - accuracy: 0.9825 - loss: 0.0534 - val_accuracy: 0.8153 - val_loss: 1.1506\n",
+      "Epoch 17/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 372ms/step - accuracy: 0.9965 - loss: 0.0116 - val_accuracy: 0.8471 - val_loss: 1.2062\n",
+      "Epoch 18/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 376ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 0.8408 - val_loss: 1.2559\n",
+      "Epoch 19/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 415ms/step - accuracy: 1.0000 - loss: 2.3890e-04 - val_accuracy: 0.8535 - val_loss: 1.3033\n",
+      "Epoch 20/20\n",
+      "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 411ms/step - accuracy: 1.0000 - loss: 1.3011e-04 - val_accuracy: 0.8471 - val_loss: 1.2932\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Melatih model dengan data train, validasi dilakukan dengan data validation\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    epochs=10,  # Sesuaikan jumlah epoch\n",
+    "    validation_data=val_generator\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "# save model\n",
+    "model.save('model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save history\n",
+    "import pickle\n",
+    "with open('history.pkl', 'wb') as file_pi:\n",
+    "    pickle.dump(history.history, file_pi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-14 11:41:05.053908: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
+      "2024-10-14 11:41:05.053947: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
+      "2024-10-14 11:41:05.053957: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
+      "2024-10-14 11:41:05.054262: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2024-10-14 11:41:05.054278: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "# Load model .h5\n",
+    "model = tf.keras.models.load_model('model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.preprocessing.image import img_to_array\n",
+    "\n",
+    "def preprocess_image(image_path, img_size):\n",
+    "    # Baca gambar\n",
+    "    img = cv2.imread(image_path)\n",
+    "    \n",
+    "    # Resize gambar sesuai dengan input model\n",
+    "    img = cv2.resize(img, (img_size, img_size))\n",
+    "    \n",
+    "    # Konversi gambar ke array dan normalisasi\n",
+    "    \n",
+    "    # Tambahkan dimensi batch: (height, width, channels) -> (1, height, width, channels)\n",
+    "    img = np.expand_dims(img, axis=0)\n",
+    "    \n",
+    "    return img\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step\n",
+      "Predictions: [[0. 0. 0. 1. 0. 0. 0. 0.]]\n",
+      "Predicted class: [3]\n",
+      "Predicted class: Daun Mint\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Path ke gambar yang ingin diprediksi\n",
+    "image_path = 'lidah-buaya.jpg'\n",
+    "\n",
+    "# Preprocessing gambar (misalnya ukuran gambar input yang diharapkan model adalah 224x224)\n",
+    "img_size = 224\n",
+    "preprocessed_image = preprocess_image(image_path, img_size)\n",
+    "\n",
+    "# Prediksi menggunakan model\n",
+    "predictions = model.predict(preprocessed_image)\n",
+    "\n",
+    "# Tampilkan hasil prediksi\n",
+    "print(\"Predictions:\", predictions)\n",
+    "\n",
+    "# Ambil kelas dengan probabilitas tertinggi\n",
+    "predicted_class = np.argmax(predictions, axis=1)\n",
+    "\n",
+    "# Cetak kelas yang diprediksi\n",
+    "print(\"Predicted class:\", predicted_class)\n",
+    "\n",
+    "class_names = ['Daun Jambu Biji',\n",
+    " 'Daun Kemangi',\n",
+    " 'Daun Kunyit',\n",
+    " 'Daun Mint',\n",
+    " 'Daun Pepaya',\n",
+    " 'Daun Sirih',\n",
+    " 'Daun Sirsak',\n",
+    " 'Lidah Buaya']\n",
+    "# Konversi indeks prediksi menjadi nama kelas\n",
+    "predicted_class_name = class_names[predicted_class[0]]\n",
+    "\n",
+    "print(\"Predicted class:\", predicted_class_name)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

README.dataset.txt

@@ -0,0 +1,6 @@
+# Tanaman Herbal > 2024-10-14 12:23pm
+https://universe.roboflow.com/object-detection-9p5ol/tanaman-herbal-nfwdi
+
+Provided by a Roboflow user
+License: MIT
+

Tanaman-Herbal-7/README.dataset.txt

@@ -0,0 +1,6 @@
+# Tanaman Herbal > 2024-10-14 11:26pm
+https://universe.roboflow.com/object-detection-9p5ol/tanaman-herbal-nfwdi
+
+Provided by a Roboflow user
+License: MIT
+

Tanaman-Herbal-7/README.roboflow.txt

@@ -0,0 +1,32 @@
+
+Tanaman Herbal - v7 2024-10-14 11:26pm
+==============================
+
+This dataset was exported via roboflow.com on October 14, 2024 at 11:27 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 2468 images.
+Leaf are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 640x640 (Stretch)
+
+The following augmentation was applied to create 3 versions of each source image:
+* 50% probability of horizontal flip
+* 50% probability of vertical flip
+* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
+
+