week-11/Tugas Praktikum-11.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQtno8acH2kQ"
      },
      "source": [
        "# **Jobsheet 11 | Pertemuan 11**\n",
        "# **Convolutional Neural Network (CNN)**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DpJhJK7zHwtJ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPooling2D, Flatten, Dense, Dropout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CdpDSc1JBdE"
      },
      "source": [
        "**Lakukan Load Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATR7H9_dJCFG",
        "outputId": "fcd3e38c-7bd0-44a6-e7cb-66d4b3b7965f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 16s 0us/step\n"
          ]
        }
      ],
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
        "\n",
        "# Normalisasikan nilai piksel menjadi antara 0 dan 1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-CgsdmLJPL2"
      },
      "source": [
        "**Sample Image**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "kTMpbNwsJRJt",
        "outputId": "08182251-150a-4d04-e60d-f0bd034c4dba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "       'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(train_images[i])\n",
        "  # The CIFAR labels happen to be arrays,\n",
        "  # which is why you need the extra index\n",
        "  plt.xlabel(class_names[train_labels[i][0]])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGwSFxdBJVog"
      },
      "source": [
        "**Lakukan pembuatan Model CNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "dly2cPlyJbhZ",
        "outputId": "db9ba4fb-29ee-4752-f187-7abf531b0020"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1563/1563 [==============================] - 32s 10ms/step - loss: 3.0109 - accuracy: 0.2954 - val_loss: 2.3744 - val_accuracy: 0.4374\n",
            "Epoch 2/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 2.5121 - accuracy: 0.4033 - val_loss: 2.1800 - val_accuracy: 0.4937\n",
            "Epoch 3/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 2.2826 - accuracy: 0.4643 - val_loss: 2.0202 - val_accuracy: 0.5420\n",
            "Epoch 4/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 2.1118 - accuracy: 0.5019 - val_loss: 1.9272 - val_accuracy: 0.5598\n",
            "Epoch 5/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.9675 - accuracy: 0.5369 - val_loss: 1.8282 - val_accuracy: 0.5820\n",
            "Epoch 6/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.8423 - accuracy: 0.5616 - val_loss: 1.6784 - val_accuracy: 0.6065\n",
            "Epoch 7/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.7247 - accuracy: 0.5854 - val_loss: 1.5237 - val_accuracy: 0.6477\n",
            "Epoch 8/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.6216 - accuracy: 0.6050 - val_loss: 1.4999 - val_accuracy: 0.6420\n",
            "Epoch 9/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 1.5277 - accuracy: 0.6214 - val_loss: 1.4185 - val_accuracy: 0.6546\n",
            "Epoch 10/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.4417 - accuracy: 0.6355 - val_loss: 1.3149 - val_accuracy: 0.6738\n",
            "Epoch 11/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 1.3684 - accuracy: 0.6483 - val_loss: 1.3884 - val_accuracy: 0.6417\n",
            "Epoch 12/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 1.3064 - accuracy: 0.6613 - val_loss: 1.1705 - val_accuracy: 0.7064\n",
            "Epoch 13/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 1.2434 - accuracy: 0.6723 - val_loss: 1.1985 - val_accuracy: 0.6894\n",
            "Epoch 14/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 1.1889 - accuracy: 0.6834 - val_loss: 1.0927 - val_accuracy: 0.7152\n",
            "Epoch 15/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 1.1443 - accuracy: 0.6891 - val_loss: 1.0727 - val_accuracy: 0.7166\n",
            "Epoch 16/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 1.1035 - accuracy: 0.6977 - val_loss: 0.9907 - val_accuracy: 0.7354\n",
            "Epoch 17/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 1.0623 - accuracy: 0.7070 - val_loss: 0.9620 - val_accuracy: 0.7394\n",
            "Epoch 18/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 1.0350 - accuracy: 0.7114 - val_loss: 0.9717 - val_accuracy: 0.7324\n",
            "Epoch 19/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.9980 - accuracy: 0.7198 - val_loss: 0.9079 - val_accuracy: 0.7498\n",
            "Epoch 20/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.9683 - accuracy: 0.7277 - val_loss: 0.8976 - val_accuracy: 0.7526\n",
            "Epoch 21/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.9449 - accuracy: 0.7330 - val_loss: 0.8596 - val_accuracy: 0.7616\n",
            "Epoch 22/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.9233 - accuracy: 0.7367 - val_loss: 0.8857 - val_accuracy: 0.7508\n",
            "Epoch 23/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.9035 - accuracy: 0.7403 - val_loss: 0.8276 - val_accuracy: 0.7667\n",
            "Epoch 24/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.8777 - accuracy: 0.7460 - val_loss: 0.8146 - val_accuracy: 0.7711\n",
            "Epoch 25/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.8631 - accuracy: 0.7490 - val_loss: 0.9169 - val_accuracy: 0.7357\n",
            "Epoch 26/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.8429 - accuracy: 0.7556 - val_loss: 0.8590 - val_accuracy: 0.7535\n",
            "Epoch 27/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.8213 - accuracy: 0.7614 - val_loss: 0.7867 - val_accuracy: 0.7755\n",
            "Epoch 28/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.8108 - accuracy: 0.7624 - val_loss: 0.7815 - val_accuracy: 0.7755\n",
            "Epoch 29/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.8005 - accuracy: 0.7648 - val_loss: 0.8030 - val_accuracy: 0.7661\n",
            "Epoch 30/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.7832 - accuracy: 0.7678 - val_loss: 0.8228 - val_accuracy: 0.7591\n",
            "Epoch 31/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7743 - accuracy: 0.7733 - val_loss: 0.7421 - val_accuracy: 0.7824\n",
            "Epoch 32/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7664 - accuracy: 0.7755 - val_loss: 0.7661 - val_accuracy: 0.7807\n",
            "Epoch 33/100\n",
            "1563/1563 [==============================] - 16s 10ms/step - loss: 0.7564 - accuracy: 0.7765 - val_loss: 0.7374 - val_accuracy: 0.7842\n",
            "Epoch 34/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.7417 - accuracy: 0.7818 - val_loss: 0.7618 - val_accuracy: 0.7809\n",
            "Epoch 35/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.7336 - accuracy: 0.7852 - val_loss: 0.7282 - val_accuracy: 0.7916\n",
            "Epoch 36/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7229 - accuracy: 0.7888 - val_loss: 0.7705 - val_accuracy: 0.7750\n",
            "Epoch 37/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.7150 - accuracy: 0.7902 - val_loss: 0.7717 - val_accuracy: 0.7757\n",
            "Epoch 38/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7085 - accuracy: 0.7904 - val_loss: 0.7229 - val_accuracy: 0.7915\n",
            "Epoch 39/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7064 - accuracy: 0.7931 - val_loss: 0.7975 - val_accuracy: 0.7670\n",
            "Epoch 40/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6963 - accuracy: 0.7930 - val_loss: 0.7088 - val_accuracy: 0.7947\n",
            "Epoch 41/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6913 - accuracy: 0.7962 - val_loss: 0.6907 - val_accuracy: 0.7988\n",
            "Epoch 42/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6770 - accuracy: 0.7994 - val_loss: 0.7683 - val_accuracy: 0.7746\n",
            "Epoch 43/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6727 - accuracy: 0.8010 - val_loss: 0.6902 - val_accuracy: 0.7995\n",
            "Epoch 44/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6709 - accuracy: 0.8034 - val_loss: 0.7221 - val_accuracy: 0.7912\n",
            "Epoch 45/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6615 - accuracy: 0.8063 - val_loss: 0.7557 - val_accuracy: 0.7804\n",
            "Epoch 46/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6618 - accuracy: 0.8070 - val_loss: 0.6807 - val_accuracy: 0.8025\n",
            "Epoch 47/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6501 - accuracy: 0.8101 - val_loss: 0.6878 - val_accuracy: 0.8022\n",
            "Epoch 48/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6467 - accuracy: 0.8108 - val_loss: 0.6889 - val_accuracy: 0.8046\n",
            "Epoch 49/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6484 - accuracy: 0.8086 - val_loss: 0.7168 - val_accuracy: 0.7971\n",
            "Epoch 50/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6408 - accuracy: 0.8138 - val_loss: 0.6957 - val_accuracy: 0.7961\n",
            "Epoch 51/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6310 - accuracy: 0.8178 - val_loss: 0.7290 - val_accuracy: 0.7917\n",
            "Epoch 52/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6403 - accuracy: 0.8141 - val_loss: 0.7120 - val_accuracy: 0.7950\n",
            "Epoch 53/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6320 - accuracy: 0.8160 - val_loss: 0.7276 - val_accuracy: 0.7880\n",
            "Epoch 54/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6291 - accuracy: 0.8176 - val_loss: 0.8097 - val_accuracy: 0.7646\n",
            "Epoch 55/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6245 - accuracy: 0.8181 - val_loss: 0.7015 - val_accuracy: 0.8027\n",
            "Epoch 56/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6173 - accuracy: 0.8214 - val_loss: 0.7267 - val_accuracy: 0.7899\n",
            "Epoch 57/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6176 - accuracy: 0.8214 - val_loss: 0.7314 - val_accuracy: 0.7880\n",
            "Epoch 58/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6108 - accuracy: 0.8260 - val_loss: 0.6807 - val_accuracy: 0.8115\n",
            "Epoch 59/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.6059 - accuracy: 0.8250 - val_loss: 0.7299 - val_accuracy: 0.7921\n",
            "Epoch 60/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6080 - accuracy: 0.8250 - val_loss: 0.6774 - val_accuracy: 0.8091\n",
            "Epoch 61/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.6002 - accuracy: 0.8278 - val_loss: 0.6694 - val_accuracy: 0.8120\n",
            "Epoch 62/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5994 - accuracy: 0.8281 - val_loss: 0.7156 - val_accuracy: 0.7949\n",
            "Epoch 63/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5974 - accuracy: 0.8274 - val_loss: 0.6882 - val_accuracy: 0.8061\n",
            "Epoch 64/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5940 - accuracy: 0.8298 - val_loss: 0.7636 - val_accuracy: 0.7821\n",
            "Epoch 65/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5874 - accuracy: 0.8312 - val_loss: 0.6789 - val_accuracy: 0.8067\n",
            "Epoch 66/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5888 - accuracy: 0.8306 - val_loss: 0.6817 - val_accuracy: 0.8107\n",
            "Epoch 67/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5855 - accuracy: 0.8337 - val_loss: 0.7109 - val_accuracy: 0.7998\n",
            "Epoch 68/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5876 - accuracy: 0.8307 - val_loss: 0.7602 - val_accuracy: 0.7837\n",
            "Epoch 69/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5839 - accuracy: 0.8353 - val_loss: 0.6945 - val_accuracy: 0.8040\n",
            "Epoch 70/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5770 - accuracy: 0.8365 - val_loss: 0.6722 - val_accuracy: 0.8103\n",
            "Epoch 71/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5809 - accuracy: 0.8356 - val_loss: 0.6784 - val_accuracy: 0.8100\n",
            "Epoch 72/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5798 - accuracy: 0.8355 - val_loss: 0.6704 - val_accuracy: 0.8121\n",
            "Epoch 73/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5827 - accuracy: 0.8343 - val_loss: 0.6835 - val_accuracy: 0.8097\n",
            "Epoch 74/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5765 - accuracy: 0.8380 - val_loss: 0.6850 - val_accuracy: 0.8086\n",
            "Epoch 75/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5685 - accuracy: 0.8392 - val_loss: 0.7377 - val_accuracy: 0.7938\n",
            "Epoch 76/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5754 - accuracy: 0.8367 - val_loss: 0.7157 - val_accuracy: 0.7974\n",
            "Epoch 77/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5657 - accuracy: 0.8407 - val_loss: 0.6645 - val_accuracy: 0.8164\n",
            "Epoch 78/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5649 - accuracy: 0.8405 - val_loss: 0.6644 - val_accuracy: 0.8162\n",
            "Epoch 79/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5688 - accuracy: 0.8407 - val_loss: 0.6934 - val_accuracy: 0.8089\n",
            "Epoch 80/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5624 - accuracy: 0.8414 - val_loss: 0.6982 - val_accuracy: 0.8039\n",
            "Epoch 81/100\n",
            "1563/1563 [==============================] - 16s 10ms/step - loss: 0.5616 - accuracy: 0.8422 - val_loss: 0.6879 - val_accuracy: 0.8058\n",
            "Epoch 82/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5590 - accuracy: 0.8425 - val_loss: 0.6796 - val_accuracy: 0.8129\n",
            "Epoch 83/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5530 - accuracy: 0.8461 - val_loss: 0.7126 - val_accuracy: 0.8029\n",
            "Epoch 84/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5583 - accuracy: 0.8414 - val_loss: 0.7136 - val_accuracy: 0.8019\n",
            "Epoch 85/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5604 - accuracy: 0.8422 - val_loss: 0.6650 - val_accuracy: 0.8157\n",
            "Epoch 86/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5509 - accuracy: 0.8452 - val_loss: 0.6876 - val_accuracy: 0.8093\n",
            "Epoch 87/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5551 - accuracy: 0.8455 - val_loss: 0.6855 - val_accuracy: 0.8145\n",
            "Epoch 88/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5493 - accuracy: 0.8468 - val_loss: 0.6750 - val_accuracy: 0.8131\n",
            "Epoch 89/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5505 - accuracy: 0.8456 - val_loss: 0.6760 - val_accuracy: 0.8185\n",
            "Epoch 90/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5475 - accuracy: 0.8488 - val_loss: 0.6738 - val_accuracy: 0.8160\n",
            "Epoch 91/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5486 - accuracy: 0.8466 - val_loss: 0.7041 - val_accuracy: 0.8058\n",
            "Epoch 92/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5464 - accuracy: 0.8481 - val_loss: 0.6817 - val_accuracy: 0.8127\n",
            "Epoch 93/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5457 - accuracy: 0.8475 - val_loss: 0.7285 - val_accuracy: 0.8006\n",
            "Epoch 94/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5421 - accuracy: 0.8509 - val_loss: 0.7094 - val_accuracy: 0.8073\n",
            "Epoch 95/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5438 - accuracy: 0.8495 - val_loss: 0.8601 - val_accuracy: 0.7659\n",
            "Epoch 96/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5428 - accuracy: 0.8491 - val_loss: 0.6810 - val_accuracy: 0.8131\n",
            "Epoch 97/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5366 - accuracy: 0.8516 - val_loss: 0.7045 - val_accuracy: 0.8085\n",
            "Epoch 98/100\n",
            "1563/1563 [==============================] - 14s 9ms/step - loss: 0.5400 - accuracy: 0.8519 - val_loss: 0.6726 - val_accuracy: 0.8147\n",
            "Epoch 99/100\n",
            "1563/1563 [==============================] - 15s 9ms/step - loss: 0.5351 - accuracy: 0.8520 - val_loss: 0.6809 - val_accuracy: 0.8142\n",
            "Epoch 100/100\n",
            "1563/1563 [==============================] - 15s 10ms/step - loss: 0.5332 - accuracy: 0.8537 - val_loss: 0.6929 - val_accuracy: 0.8114\n"
          ]
        }
      ],
      "source": [
        "# Model definition\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "# Model compilation\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Model training\n",
        "history = model.fit(train_images, train_labels, epochs=100,\n",
        "                    validation_data=(test_images, test_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoKP4vOiRZOy"
      },
      "source": [
        "**Cek Arsitektur Konvolusi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGqVSXrPRXzv",
        "outputId": "663bd679-46aa-46eb-859c-193e121143e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 30, 30, 32)        128       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 15, 15, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 13, 13, 64)        256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 6, 6, 64)          0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 4, 4, 128)         512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 2, 2, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 2, 2, 128)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               262656    \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 512)               2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 256)               1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 493770 (1.88 MB)\n",
            "Trainable params: 491786 (1.88 MB)\n",
            "Non-trainable params: 1984 (7.75 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCF6CLMMRhf8"
      },
      "source": [
        "**Evaluasi Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "We4QqddMRgav",
        "outputId": "8624758d-6653-4797-8e09-1d498e7e7a6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.6929 - accuracy: 0.8114 - 838ms/epoch - 3ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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Q+/XlO9KY8OlSElNz8Lbb6BvfmM6xYXRuanpYgv28SUzNYcvebLbtyyYxNYfcQidFDidFDotCp5M9mfllVtnt0TyMx0Z3pXvzRrX4jcUTKLipgIIbEfF0eYUOdqXlEhHkR2iAt7vHY01SBi/9toGZq5JL7G+3wdk9m3HTiW34a/1eJv+8hkKHRbNGAbx6cS96tWh8RO1Izylk1a50Vu5KZ+PuLPq0bMz5feKUGyNHRMFNBRTciIinSc0uYOHWVBZv28/Crams3JlOocP8avf1thMV4kdYgA+rdmUAZir0md2bcmb3WD5bkMgf6/aUes+RXaJ55rwe5Q4bidQ2JRSLiHi4giInv6/dzZeLtjNn/R4chyyi6O9jJ6/QFLXbsT+XHftzsdng9G6x3HpSO9pHmzWHRnaJYcWOdF7+fQOzVqfg62XnvjM6cdmglspxkQZLwY2ISD2QkVfI5wu2s3xnOrkFDnILi8gpcJBX6CQswJvoUH+iQ/2JCvEjOT2Pb5fuZN9BizC2jQqmX3w4/eIb0y8+nOaNA8gvMrkvuzPz2JNZQLvoYNo0CS712d2ah/H2ZX3ZvCcLHy87ceGBtfnVRaqdghsRkWqWX+RgxY50IoP9iAsPrDDHZGdaLu/9s4XPFm6vcuXdJiF+nNe7ORf0bV5m0OLv40VceGClg5XWZbyHSEOk4EZEpBpt3J3FTZ8sYV1KJgB+3nbaNAmmXXQwTYL9sABXpmNyRi6/rEpxDym1jw7mnF7NaRToQ4CPFwG+Xvh520nPLSQlI4/dGfmkZObjdSBn5oQOTfBWuX+RUhTciIhUk+8TdjLpmxXkFDgI9vOm0OEkv8hpCuAlZZR73JC2EVxzXGuGtW+iPBeRaqDgRkTkKOUVOnjkh1VMW7AdgEGtI3hpbE8igvzYnprDht1ZrE/JJCOvEAAbNmw28PGyc0rnaLo2C6vL5ot4HAU3IiKVlJSey0u/bWD22t1YFnjZwW6zkVfoYH9OITYb3Dy8Hbee1M6dZxMfGUR8ZBAnd46u49aLHDsU3IiIHEZ6TiFv/LmJ9+ZuIb/IWeY+kcG+vDimF0PbRdZy60TkUApuRMTjWJbF5r3ZtAwPrFLCbZHDya60PFJzCtifU8D+7AK27s3mg3nbSM81Q0r948O55aR2RAT74nCaFbKdlkXbqGCC/PQrVY5QymqY8yTEdIdel0JobF23qEHT/0QR8Shb92Yz6ZsVzNu8j44xITxyVhcGtI4oc1+n02L97kzmbtzHvE17mb85lcxypmN3iA7h7tM6cGKHKCX91qWcVEjfDt4B4O0H3v7gFwy+QYc/tr7a/Cd8fgnkZ8CaH2DOU9DxdOhzBbQ+EeyaEVdVWn5BRDxCkcPJO/9s4YVZ60sNHZ3dsyn3nt6J6FB/cgsc/LNxL7+uSuaPdbvZm1VQYl8/bzuRwX40DvKhcaAvjQN9Gda+CaN7NdOaSHUtPwte7gnZhywXYfeBEQ/B4Jtrv01Jy2DRVBhyG4S3qvrxyz6D7yeAsxCa9wO7NyTOK349qgtc9j0EN6m2JjdUWn5BRI4pq3alc/fXy1m500y3Hto2krtO7cDnC7fz6YJEvk/YxW+rU+gbH878LfvIKywOfgJ8vOjXKpwhbSIY3CaSzk1DFcTUVxt+NYGN3cf01BTlQ1GuCQx+vR/CmkOXc2qvPZYFP9wKu5bCxtlwxU/QqEXp/Tb9AfPfhOAoiBtgbhFt4e/n4PfHzT5dzoXRb4CPP+xeA4veg2XTYPcq+O0hGP167X2v8mz4DWbebc57YDgERkJgBES0gd6XQWjTum6hm3puRKRB+3bpDu7+agUFDidhAT7cf0Ynzu/T3D10tGJHOg9OX8nSxDT3Mc0aBXBy52hO7hxNv/hwfL3V7d8gfHk5rPoWhtwKJz9qtlkW/HIf/PeaGaK64ido1qfq712QDXNfhlbHQfzQyh2zdS68f3rx88atzOcffJFf8Db8fDdYjpLH+oZAgSn0yJBb4aSHSw8/bV8I744wj6/8BVoMLN2G1C2Q+B90H3N0w1e718Ifj0Pv8dDu5NKvb/gNPrsYHPllH2/3gW4XwOAJEN3lyNtRAa0KXgEFNyINS5HDiZfdVirPxem0mDJrPa/+sRGAEZ2iePLcbkSF+Jd6D6fT4qeVSezcn8tx7ZrQKTakZvNmMnZBwqcw4DrwCzn8/k4nrPsJ/nsd7F4w9nPwbYDrO+1eCyu/NgFIzl7ofLb5i75pb7MU+dEozIVn2kBhNlz9OzQ/KIBxOmDaWNjwCwRHwzW/m16cynI6TM7Lup/APwwmLDK9LIcz7WJYNwM6jYLkFbB/K0S2h8t/goDG8Mu9sOB/Zt+u55s2bV8Au5ZAUR7Y7HDaM9D/mvI/Y/rNsORDiO4K1/4JXgcNuKRugXdPNr1Zpz1j/r0dqZ/vNr1LAINvgZMeBK8DK8Jv/M18V0e++a5Dbjc/35x95rPX/wLb5ha/V9sRZoiw1bCj/7kfRMFNBRTciNRf8zbt45dVyexKyyU5I49daXnszconOtSPM7s35aweTenePIy8Qid3fJnATyuSAbjxhDbceUoH7PVlOOnra2DFF+Yiccpj5e/nKIQVX8I/L8LedcXbz30bul9Yfe1Z9zP89jDEdDPvfaQXHMuCxe+ZYRbfIPA9KJF3w6+we3XZx0V3NUFOu5OhUfyR9TCs+RE+HwehzeH2laW/Q34mvDvSDONEd4MrZ5pE48o4+MIO0P0iOPd/FR+zbxO80gew4KaFJrn5vdMhY4f5viExJigAOOkhGHp7cZuLCkww5ON/+F6O7H3wah/I3Q+nPgUDbziwfS+8ewqkbjLPg6Ph1mXgE1C573yoT8fA+pnFz5v3h/Onwt71JnB05EPHM+GC94uDnoPtWAz/vgxrpoPlhLA4uGVp2fseIQU3FVBwI1L//Ld5Hy/MWs/8LamH3Tc+IhBfbzvrU7Lw8bIx+dzunN+nCn+l1zSnE55rZ/6ybdzK/IIvK5jYsw4+Ph/SE81zv1DzV//OReYv30u+Pvq2ZO81F+6VXxVvu/BD06NSVZZl8kP+fq78few+pu1dzzU9HwmfwurvTS+Fi08QRHUyF/XmfaHnONNbdTjfXAvLP4eBN8Kpk8veJy0R3j4JsndD+1Phgg9MAFGR/96AmfeYx8fdCX8/D1gw/gdodXz5x824Exa+De1GwrgvzLZ9m+C90yArxTz3DoBz34LOZx3++1Vk0Xvw421mKOvmRaY38INRsHMxhLUw7U3fDiOfhEE3HdlnvDYA9qyF/teZJOf8dPBvZH52RXnQ4QwT2Hj7Vvw+qVvMOW3SAfpddWRtKYeCmwoouBGpewVFTlIy8ti4J4u3/tzMvM37APD1snNu72Z0aRpKTFgAsWH+RIX4kbA9jR+WJ/Hb6hRyC03uQniQL/+7tA/94sPr8quUlrwS3hxS/PyGf8v+69yVPxLUxFyw+11lgpFXepvhijvWVW5opCyWBSu+gp/vgtxU835Ne5mLYeN4uGmB6WmoijlPmzosAIMmmLblZ5lclaJck+fS8QwzHHOw3P2mLcs+M70Vh+ZsjHoZ+oyv+LOLCuDZtuaCe8VMaDmo/H13LDI9KI58aNYXxnxcfs2YNT+a4Sgsk8Mz5FaYcQcsfAci2sENc8s+Tzmp8EIXKMwpHQTtXgsfjQabF1z0sTnvR8vpNLk3OxebxOOCbDMEF9AYrpplZldNv9n8W7p1Welp8ZkpsHEWdD2v7J4dy4Inm5rvc/MSE2x+eYUZPgNof5oJig8X2NQwzZYSkXplb1Y+7/6zhb/W7yE5PY992SWnX/t42RjTL44bT2hL00alf/me0iWGU7rEkJ1fxG9rUli+I53LB8cTF14P81K2/Fny+ZofSgc3eemw9ifzeNxX0LSneewfZi7IOxeZgGDQjaXfP/E/2LnEDE+UN7z017PwxxPmcXRXOOsV0yv0Sh+TFzL/fzDklsp/p7+fLw5sTnnCJI1WVkBjk1PS/xpwFJlhlJSV5rys+tYk3Pa+rOKhsi1/msAmONrMNKpI876mJ+WL8eY8vnUCXPSJ2e7idMLmP+DrqwEL+l5phhABhj8Aq6fDvg1mmOX4/yv9GYvfM4FATDeIP67ka1Ed4ZYEM6Xbq5ousXY7nP4cvD0cVn1jtnkHwMVfQGQ7E7D+PQX2bzHnc+htxcdmJsPUkebnXpQH/a4u/f7Ze8z3wWaGk7x9TQLz3BfNcN/w++s8sKkqTREQkRqTlJ7Lw9NXMfTp33ljziZW7cpwBza+XnbiwgO4ZGAL5vzfiTw+ulvpwCYvHRKmmSRMIMjPm7N7NuOBMzvXz8AGTEE2MEEFmN6BQ63+3vQsNOkIsT1KvtZ9jLlf/nnp43JS4ZML4ZdJsGFW2Z9vWSYBFUztlWvnQLPeJv/kpAfM9r+eNb1ElTH3ZZh9YGbSiIerFtgcysvbDFd0PQ/OmGJmN6WsgO3zKz5u9ffmvuOZlcvXaX0CXPsHNOkEWclmqGjJhyYH5sfb4YXO8PG5psep7clw2rPFwVVAIzO8A/DXc5C6ueR7FxXA/LfM40ETyg7KfPyrL7BxadbbBGFgeuLOnwpx/c1zLx8Ydrd5PPclE5AA5KbBx+eZwAZMr2JZXK+HNisOYrx9YdhdJmesqr189UCdBzevvfYa8fHx+Pv7M2DAABYsWFDh/i+++CIdOnQgICCAuLg4br/9dvLy8io8RkRqj2VZrNyZzqRvlnP8M3/w/r9bySt00iOuES9d1JMZtwxlyQMns+7xU/n7ruE8ProbzQ4OaiwLtv0L314Pz3WA766H988w02Lr5guZW2U4CotnjZzyuBmaSFlh8hAOtuxA4NL9wtIXx67nmr/6kxJMXs7B/nrW9GBA6R4il7RtJv/C7m0uTgcndPa42JT3z8+AP548/PdZ8wPMOhAQnXifSYqtLoHh0O1883jhO+Xv5yiCtTPM46rkroS3hqtnmYDIUWCGbT4+zxTcy0wyuT89xsIF75UORLqdb2b6FOWZ3BpXsABmNlhWMoTEmiGi2jTiIdPLdf57poJxiTZfYGrn5Kaa5OjCXJMInLISOPBvbN/Gst93/zZz3zi+plpe6+p0WOrzzz9n4sSJvPnmmwwYMIAXX3yRkSNHsm7dOqKiSo81f/rpp9xzzz1MnTqVwYMHs379ei6//HJsNhtTpkypg28gIi77svL5LmEXXy7aztrk4ovBgFbh3Dy8HUPaRpScfp29Dz462/QguGfeBJsLj2sGCIBfmLmgfz4OrvkDwprV3pfK2AVvDDG5JGe/evj9dy6BgiwzFNNqGMQPgS1/wdofi6vnpm2Hbf+Yx93KmBEVFGmSctfPhOVfFPe2pG42Qw4u5QU3Ww+8d7M+pXMv7HaTjPv+GWZopf81Jrm3PEs+Mvf9rjGBUnXrdw0s/RhWfWd6S8rKMdo211ywA8KhZSXrz7j4hcCFH8Ffz8CfT0NQFHQ4zfw8448rP9nYZjM9S28Mgk2zYXJzM0urSQfYu8Hs0//a2h+q8Q8zQ4xl8fKGEybB11fBv69A4nxI/Nf8/zn5EZOQvG9T2ce6em4at6yJVteJOu25mTJlCtdccw1XXHEFnTt35s033yQwMJCpU6eWuf+///7LkCFDuPjii4mPj+eUU05h7Nixh+3tEZHqZ1kWa5MzeOfvzVz+3gIGTp7NYz+uZm1yJr5eds7oHssX1w3i8+sGMbRdZOm6Mqu/NQmmmUnmL8qkBHPRT91k/qrufRlc9RtMXG1K0GelmCJihbm19yU3zzEX1qUfmTL7h+MKOOKPM4FEx1Hm+cFDUyu+KN6nUVzZ7+OaBr78C5MfAjD7MVOJ11WgLnmFGaY6lCu4Ka8QXfxQ05thOU1V3/IUZBd/H9dwSHVr2tPkGDkLi4fSDrVmurnvePqRDfXY7XDCPXDXFpi4Bka9aKakH24WVWRbk+wcHG2eZ+wwgU56IvgEQp/Lq96WmtblHDPUmZduEoi9/eHiz4pnx2XuMkngh0rbau4beU5wU2c9NwUFBSxevJhJkya5t9ntdkaMGMG8efPKPGbw4MF8/PHHLFiwgP79+7N582Z++uknLr300nI/Jz8/n/z84uz8jIyM6vsSIseCvAPDIP5hACzbnsb7/27l7w172Ztl/m+dZ/+LO+w7mdn0as7tF89ZPZrSKPAwf9VunG3uB9xghhvys0yvh90L2gwvWfxu7DR4+0QTAH1/E5z3brUWByvXwcNCfz9vZoxUxJVv0/oEc9/xDPj5/0xOSWaK6ZlwD0mNKf992p9mpv2mJ8L2/8DL70Aiqc1ccL++Gvasga1/l5zWbVkHBTfHlfnWgJkZtP4Xk4OycTa0PamM7zLHDMs0allx787R6n8NfLvITHceclvJAMbpLA4MOx3B9PWDBTSq+jE9x5pbTqqp97JnnQnEWw0zw2r1jd3LBHJfXm6GRC/4AFoONq8FRpiie6mbIbZ7yeM0LFV99u7di8PhIDo6usT26Oho1q5dW+YxF198MXv37mXo0KFYlkVRURHXX3899957b7mfM3nyZB555JFqbbvIMcFVsO3XB8A3mHUX/c1zvycya3WKexd/HztD40N4eudUvJ0FXN+pNQx69PDvXVRghmsAeow5/HTZxi1NYPHh2SbnIboLHHfHUXy5Sjo4uFk93Txv0qHsfQtyYMeBXmRXcBPWzPS07FxsKtk27W2K9Xn7V5w/4htoXk/4xEyhdg2F9LwYYrqaqcd71sCWQ4Kb/VsP5Nv4FCebliWijZl6Pv9Nk+9SVnCz7sBsrg6n12wg2Xm0qeSbscMMxXU6s/i1HQtMfotfKLQeVnNtOJzAcLP0QVnLH9Q3nUebIbXw1tDmxOLtEe1McLNvYwXBjef03NR5QnFVzJkzhyeffJLXX3+dJUuW8M033zBjxgwee6z8CqCTJk0iPT3dfdu+fXsttlikgUpLNIHEj7eb3pSsZB56/QNmrU7BboNzezdj2jUDWfbQKbwz3MLbeWBq99yXyp/Fc7AdC8z7BkZCTI/D7w9mOOX0Z83j2Y+ZYm3f3WQ+c/0vZQ/RHC1X1eDQZoAF/7xQ/r6J80ziamhzc2Fx6XjgYr3mx+IZUB1Oc/eElcvVs5Pwicmd8PY3Sb1QXFfFFSC6VJRvcyjXUNP6X0yv0sGcTrMdoMOpFb/P0fLxh14Het8PTizesRh+utM8bn9qg5yxUydsNhO4HhzYgEk2htJJxY5CE1iCem6qQ2RkJF5eXqSklPxPlZKSQkxMTJnHPPDAA1x66aVcfbWZp9+tWzeys7O59tprue+++7CXMUXQz88PPz/9pxCpFMsys0lmPQgFWRTY/NjhDKe1LYlB9tVEdDmJ209uR9uog4aMXLODvP3NMMa318H1/1S8QrCrLH2b4VUrxd/3Sti70SySuHORubn4BptaID0uKrunIXmlqeVRUY/GwQrzihMtz5gC08aYHJgT7in7IuDKT2l9yHo6nUbB7EdMIJKUYLZ1v+jwnx8/FEKamjwJMIX+XMnU8UMAmwm+MpNNqX84fL7NwZp0MCX2dyyA5Z+ZAnYuOxeb2id+YdBySPnvUV36XmmC1M1/mMUoEz4xNzA/17Lq/UjVRLQx94cGN+nbTf6Vt39xfpEHqLOeG19fX/r06cPs2bPd25xOJ7Nnz2bQoLKrT+bk5JQKYLy8TNnuY6zQskjNmP8/mDERCrJY6OzAKXlP8lbRGQBc3XwHr43rXTKwgeIL6oiHzTTjnH0mJ8RRVP7nuPJtyhoOOZxTnzRr+VzwPpxwr5mOG97a9AR9d72ZLZKbVrz/3o3wxWWmavC7J5thnsrYt9H80vcPg/YjTSBmOcw6UGXZPMfctzpk+CSyHUR2MEmzOftM7kNlvrfdq3iqdGBEycJsAY2Lhxa2/G3uS+TbVHJWUa9LzP3Sj0tOd3cNSbUbUa1rA5WrcUtzjsGssu0KbHpcDDcvrp4qv8e68npuXENSjVrWTh5bLanTqeATJ05k/Pjx9O3bl/79+/Piiy+SnZ3NFVdcAcBll11Gs2bNmDzZrCMyatQopkyZQq9evRgwYAAbN27kgQceYNSoUe4gR0QOLy2ngE/mJ/LJf9tIzsjDy27Dy27jI/v79LPBW0VnMLloLMe1j+bS/n3gq3cI2r3U5JUcvFp1UT7sOFB/ps1waHcK/G+Y6c356xk4sYx8uKzdkLy8+Jgj0aS9ubk4HaZC65zJJidn+0I47SkzRLbkQxOUuHx/k+npcOXFlMc1JBXZwfzSP/7/YNPv5sI77K6SPVM5qZB04DuVtR5Rp1Hw94H363pe5QOGwTeb3qPe40sPY7U63szg2vIndL/A7Jex4/D5Ngfrco5ZV2nvevNzdB3nWkCx/WmVe5/q0O+a4s9t2ssU1ovrV3uf7+kODm4sqziQ8cBp4FDHwc2YMWPYs2cPDz74IMnJyfTs2ZOZM2e6k4wTExNL9NTcf//92Gw27r//fnbu3EmTJk0YNWoUTzzxRF19BZHal7rFXKB7X2aGYCprw29k//s2rwRO4IPl2e41mgCcDgvLUUhXP1MHY33T0Xxx+hCzbpNlwa/NzYVz+38lA5Kdi81QVFATU97fZjNTbb++Cv58xszUODSI2PS7uY/pfuRrJx3K7gXD/s/kGXx9lfmF/dnFxa+3P9WU1f9nigl+Pr/UrBpd0YrMrmRiVwJxy8HQYrDJf/n3VdOD5LL1b8AygVBZ6xh1OrN4wcnKDEm5BEfBmI/Kfq3VMFPPZOuBnpuq5Nu4+IeaBNRln5rp7nH9zb+v3avNbJt2Iyrf1qPV9iRTw8Un0PTGHcnK4VK+8FaAzcx+zNln6imBKfoIHjUNHOrB2lITJkxgwoSyy3nPmTOnxHNvb28eeughHnrooVpomUg99dOdpmdk30boen6JqbM703JZvG0/S7btZ+n2NNJyCsgvdFJYVMT3jptobtuDV2EouY7RdI4N5erjWjGkbSROy8KWlEDAZwU4/cJ47vrziy8uNhu0Og6WTTNDIAcHN1sP5Nu0HFL8l2C3801+yZIPYPotMGFhyWRQ95BUDVw4m/c1+T4/3WUu2M37mwJmrumwo98wybPb/jErcl/9W/lFAQ8NbgCOvwM+/tfkJcUPgdYnmp6sQ6eAHyq2pyn6ZrObMvrVocVAE4Ds32qGFqo6JOXS6xJzrlZ+A6c+Vdx70nJw6UUwa5LNZgJ2qRk+AWbdqPRE87vDFdx44DRwqAfBjYhUwfpfi5Nxs1Jg8x9ktTiR1/7YyHdLd5KUXvZSJEPsK2juuweAM0I2MOSCAQxqfUjF4LUrALDH9Sv9V3P8geDG1Uvgsq2cC+rIJ2HDr+avwv/eKM4XcTqLe26OJN+mMvxC4Jw3YOQT5uJ88Hf09jMrNU89FfashU/ONz04Zc1c2rve3DfpWLytzUlmOveuJaZnyDvABHuu1ZPLm65ssxXP9KoufiGml2bHAhNMHmlw03KwyVlK3WzWcHJPAa/FISmpHRFtioMb17R2Dx2WqnK/X3x8PI8++iiJiYk10R4RKU9RgVkwEcC/EQDb/3iXE5+bwxtzNpGUbnJnujcP4/LB8bw8thdf3zCIHyYM5Y3Oq9xv07loLYNbhpSuGOyq0dK8jHyNVgcKwu1cUrzOTlGBKfEOpS+ofsFw0oEe1r+eM3k2AMnLIGevmQFT1udUp8DwshMkAxrDuC/NzJDdq+GX+0rv4ygqri0TeVBuj81mCgr2v878FVyUa+rXZCaZXpnamFl0MFd+z9KPDsq3Ocyq2Yey2aDnOPP4vzfMul6g4MYTlZVUnOaZPTdVDm5uu+02vvnmG1q3bs3JJ5/MZ599VqICsIjUkIVvH+hObsLGE98AIGrnb+RnptIqMog3L+nNiodPYfqEoTx8VhfO6tGUPi3D6RbuIHTLgaEGb39zQd65pPT7H1h5u8wkzkYtzJi85YBtByqI71pq3iswomTvhkv3A8X5CjLh98fNNteQVKthtb8uz8EatYDRr5vH634uXuLAZf9WM7vJJ9AEMQcLiYHTn4HbVsB1f5v1fOIGmKKCR1IF92i4gk7XqtrN+5ZM+K6sHmNNcJa8HJxF5ud5cK0e8QyHBjf5mSb/Bjwu5+aIgpuEhAQWLFhAp06duPnmm4mNjWXChAksWVLGL0wRqbz8TJP74OrpcMneC3OeBuCzkPGM+NbJOmdz/GyFvNJjKzNvO45Tu8YS6FvGSPPyz01xudgeJrEWSg8vZe0+8Beczaz1UxbXhXTrgcJxriGpg/NtDma3mxwOMDOWkpYflG9zhLOkqlP88SZ4ydlrenAO5p4p1a78xFabzUzHPuEeuOpXGF7BOk01JW4AeB0UJFZ1SMolrJkZcnNRr41ninQFNwcW0HTl2wQ0NsnlHuSI09F79+7Nyy+/zK5du3jooYd455136NevHz179mTq1KmqOyNSlkN7CNzbHWYF5lf6wFdXwEs94fcnIM+shZb/66OQn84qK557t/YEbKyPMZVvh+X8hp93OaUQLKt4QcLelx0UoBwS3Lh6baI6lf9LzlW/xVVXxZVMXNEFtcVAM/MFC2bcUTz01aaG8m2qwtu3ONHYVaPGZc+BJWAiy1lqob7wCSg5DHWkwQ0U17yB2p0CLrUn4qDgxuk4KN8mvq5aVGOOOLgpLCzkiy++4KyzzuKOO+6gb9++vPPOO5x33nnce++9jBs3rjrbKdLwLfsMHouE1weZPI+Ns80K11v+hreGwfQJJknYNxgKs+GvZ7Be7smqD+/Ae5kJUB4uuIyBbZrw481DGXXp7WYoYfv84r/EDrVzsemV8PY3M6tciyluX2Bq1Li4823K6bWB4mOTl0P2Pkj8zzw/XJ7JyY+YhR93LDBDHuFtDkxLrQdcs5tc1YVd9riSiet5cAPFeTd2n6PLY+pwmpnVFTeg4n8H0nCFxZmePkc+pO/w2GngcASzpZYsWcJ7773HtGnTsNvtXHbZZbzwwgt07Fg85n7OOefQr5+KL4mU4Comt3u1uc179cAvmgPrMvmFmVot/a+F9b9gzX4U274NdNls1tv502coN469lBPaNzmQDBxmZups/M3MZCprWGTJB+a+82iTD+IfBkFRkL0bdiw6UMYfU/QOKr44hsaav/z2bYT/XjcBWEBjiOpc8fdu1MIUo3PVeampWVJHwtUbtXWuWWPHVVzP1XPTEIKbjmeamkIdTj2yfBsXbz+47s/D7ycNl93L5FLtWWv+H3voNHA4gp6bfv36sWHDBt544w127tzJc889VyKwAWjVqhUXXVSFQlUini4/q3jo57RnoeclEBJrAhubF/S7Gm5ZYoIAbz+y25zOtcGvck/h1SRbjcnxCWfoDa9zYoeokrOceow198s+Kz3klZ8JK742j131Q2y24qEL19RhR6FJDobDV7Z19d7Mf9PctxxSuWJrQ2+H4APrH7nK7NcH0V1NQnRhtgn2wJxH10ypshKl65vozia5+Zy36rol0hAcPDTlodPA4Qh6bjZv3kzLlhWfiKCgIN57770jbpSIx0mcZ2bfNGoBA6412yzLXET9gkuU8k/JyOPK9xeyalcGft4jGHbeRE7rFGFWTz5UxzNMj0/6dpPge3Dp/1Xfmot2eJvi3BIwwc2qbw7k3dwNySvMrCf/MIhoV/H3aHUcLH7PrOMElZ/67BcM438wQ1r1Id/GxW4352zVt2ZoquUgyNhpzpvdBxrXk+GzwymrKrJIWQ5eQNNDp4HDEfTc7N69m/nz55faPn/+fBYtWlTGESLiTlg9uIKtzQZN2pPtF8Wy7Wl8sWg7T8xYzdmvzmXVrgwignyZdu1ATuverOzABkxCaZfR5nHCtOLtTudBicSXlpzN5AqAti8wK1+71oZqXkbxvkO5em7cz6uQwNqkvaleXN8W53MNTbl+Rq7KxBFtSlR/FvEI7p6bDSUXzfQwVf6fe9NNN3HXXXcxYEDJQlE7d+7k6aefLjPwEWnQ/ngS5r5sVoh2sdnMkNCoFyv3HoeU50/cl8MPy3fxw7JdrE3OLLV7myZBvHd5f1pEVCKHoufFJrdm1bfmL7H0HaaonGvIq8fFJfePaGsK2GWlwM5FxcNllUlGDY6CJp1gzxrT01PR2kwNhSvg3LHQDB+6poE3hHwbkapyBTfbF5oeW2ylazl5gCoHN6tXr6Z379Jro/Tq1YvVq1eXcYRIA+YoNFVbi3JLv7b4PbNQY+ezK36PrD2QYpY2+HR3PJ+/Npdl29NK7BIZ7Ef76GDaR4fQISaEM7vHEuJfyZWj4wYUJ/pum1u83WaHAddDSHTJ/W020wOz8iszU8s1U6qyKzC3Os4ENy0GmwTFhi68lRkuTEs01XkbyjRwkSPhCm4KDvxRFda8bgtq1pAqBzd+fn6kpKTQunXJ6pVJSUl4e6sLVzzMjoWQn2GSTq/9s3hIZf7/4N+XYcadJlAIDC//PQ5MM97s1Yp7f00GwG6DwW0iOatHU4Z3iiIy2K/84w/HZoOLPjWzpoKjIbSZKcoWEls8++dQ8UNNcLPqG3NRr6h436EG3wyZyaYir6doNcwsYbDlz4Y1DVykqoKamDy9/HTz3AOHpOAIgptTTjmFSZMm8f333xMWZhabS0tL49577+Xkk0+u9gaKVJudi2H+WzDwBmjas3LHuBapbDMcGh3UdTv8flj/ixnCmDkJzv1fuW+RueY3QoDf8jsTEeTLzcPbcnr3WKJCysmjORJNOlTtYuzKnXEtDllR8b5DNWoBYz6qWvvqu9YnmOBm8xwzrAcKbsQz2Wwmn8y12KsHJhPDEQQ3zz33HMcffzwtW7akV69eACQkJBAdHc1HH3nYLzzxDE4H/PMCzJlsisgV5cGFH1TuWPdyASNKbvf2M2sTvXsyLP8Mup5b5hTnFdvTiFhtgpv1QX34+prBxEcGHd33qQ4RbUzPTmaSed78GK9L5UoqTllp7m324u57EU8T0fag4MYze26qPFuqWbNmLF++nGeeeYbOnTvTp08fXnrpJVasWEFcnOclJUkDl74DPjgLfn/MBDZQeh2h8mTtgaQE87hNGWshNe8LA280j3+4DfLSS7z894Y9/N/b39GUPRTizV3XXV4/AhsoWe8GDl/fxtMFNzE1b1watTQz0UQ80cGBu4aligUFBXHttddWd1tEqtfaGfDdDSbo8Aky1X9/e9gUryrMK396tcum3819THczS6gsJ95nPmf/Fvj1ATjrZZZtT+PVPzYya3UKF3stBzvY4voRFR5RrV/vqMUfByu+NI+P9Z4bML03rp4bDUmJJ3PVugENSx1q9erVJCYmUlBQUGL7WWedddSNEjlqWXvgy8vNdOimveG8d0zZ8X9ehLw0k2sS273i93Dl2xw6JHUw30A4+1V4/wxY8gEvJLbmpR2mEJ7NBhdFbIIM8K4Pq2AfqvUJZvmHoKjDF+87FrQeBv+9Zh4ruBFPdnDPjYcOSx1RheJzzjmHFStWYLPZ3Kt/u0rCOxyO6m2hyJFYNs0ENrE94apfi2cNRXcx06V3r644uHE6i3tuKlgLKT23kOnJcTTyH8WovB+4bs+T/On1AK17HMeNw1rT9v2bzI6unI76pHFLc278Qiu3hIKnazkY7N5m+FLTwMWTRbY3M0D9QswMSw9U5eDm1ltvpVWrVsyePZtWrVqxYMEC9u3bxx133MFzzz1XE20UqRrLMjNfAPpcXnI6dFSn4uCmIsnLIGcv+IaUKm63NyufVbsy+G7pTn5akUR+kRNvLiDMdxvH25fzdeiLeI04E/I2Q+5+8x7NSteGqhea9qrrFtQffiHQ6SxYP7NqlZdFGhrfQJiwyPxurG8Vw6tJlYObefPm8fvvvxMZGYndbsdutzN06FAmT57MLbfcwtKlS2uinSLFnA4z8ym0KfS9svTr2xeYYSefQOh6XsnXXCtYpxwmuHENSbUexsqUXL5YtJ51yZls2J1FanbJodj20cFc2DeOLl2+gy/Oxit5BXxyvln3CcyFsrx6M1K/nPuW6fHzrSeJ3yI1paLaXB6gysGNw+EgJCQEgMjISHbt2kWHDh1o2bIl69atq/YGipQy71X461nzOLYHNOtT8nXXmkpdzildu8UV3OxeU/FnHJgCvja4P+e98S/5RcVLL9hsENc4kCFtI7iwbxw94xoVr9Q97it4Z4SpFjz3JbPt4PWkpH7z8lEgKuIBqhzcdO3alWXLltGqVSsGDBjAM888g6+vL2+99VapqsUi1S5lNfz+ePHzmZPgyl+Ku1bzM80aSwC9Li19fFQnc5+xA3LTIKBR6X3y0t3rLV33byPynU6OaxfJ2T2b0SE6hDZRQQT6lvNfJyTGBDhTTymeGt66HubbiIh4sCpnEd5///04neav2EcffZQtW7Zw3HHH8dNPP/Hyyy9XewNF3IoK4NvrzLBB/HFm2Gn7fLOEgMvKb6Aw28z+aTGw9HsENILQ5uZxeb03m/8Ey8EmZyzbnJGM7tmUqZf34/w+zenWPKz8wMYlqqNZDsHb3ySmNul4RF9XRESOTJV7bkaOLK7C2rZtW9auXUtqaiqNGzcu7poXqQl/PQPJyyGgsZnaveRD+OMJmPUQdDjdFF1zJRL3vrT8RLmoTqbnZvdqaDmo1Mtr/v6GTsCfzh5cMrAFj57VFbu9iv+244fCrctMm/T/QkSkVlWp56awsBBvb29WrlxZYnt4eLgCG6lZOxbD31PM4zOmmOGfwTebXpj07SYPZ/cas9Cl3Rt6jC3/vaJdeTelk4rfnLORkF1/AxDS9VQeO/sIAhuXkBjwDzuyY0VE5IhVKbjx8fGhRYsWqmUjtasw1wxHWQ4z+6nruWa7TwCc/Ih5/PcLxUnG7U8tv6IwlJtUPG1BIl/+8jvNbXspsvlywbljFLSLiDRAVc65ue+++7j33ntJTU2tifaIFHM6Yd3P8MEo2LcBgmPg9ENqKXU9z9ShKcyGlV+bbWUlEh/MPR18lamJA/y4fBf3fruCkfZFAHi3HmpqQYiISINT5ZybV199lY0bN9K0aVNatmxJUFDJehBLliyptsbJMaowz6y0/e+rJqgB8DqwCvehtRlsNjj1KXjnwPIGwTEVL5cApjqnzcssw5CZzJ/J3tz+eQKWBZeGLII8zDRyERFpkKoc3IwePboGmiFyQPZeeOck2L/VPPcLhb5XwIDrTdG+sjTvAz0uhmWfHqhIfJh/1j7+ZuG4vetZu+w/rv81gEKHxdUd8ojdtgnsPtBpVHV+KxERqUVVDm4eeuihmmiHiJHwqQlsgprAkNug92WlC/GVZdSLprelTeUWqNwf3I7Ge9fz9cxfyXWcybD2TZgU9wtsw/T8BDQ+ii8hIiJ1SavlSf2y4gtzf8IkGDyhcoENgLcftD+lwl4by7L4e8MeLnprHu9tCACgg30HZ/VoypvjeuO16kDOTrfzj+YbiIhIHatyz43dbq9wBolmUskR270WkleYqdzVnPPidFo8+uNq3v93KwCNvVoAcGbMfs4f2wt2LoH9W0xhwA6nVetni4hI7apycPPtt9+WeF5YWMjSpUv54IMPeOSRR6qtYXIMWvGluW97crUu6lbocHLXV8v5dulOAMYPaskN3ePhgyn4799gFuJ0zbRqf6oWTRQRaeCqHNycffbZpbadf/75dOnShc8//5yrrrqqWhomxxjLKg5uqnFYKK/QwYRPl/LbmhS87Daev6AHo3s1MwGNdwAU5cK+TWbZhmr+bBERqRvVlnMzcOBAZs+eXV1vJ8eaHQshbRv4BpulFKpBZl4hl7+3gN/WpODnbeetS/uYwAbA7mXWgAJYNBUyd4Ff2OGnkYuISL1XLcFNbm4uL7/8Ms2aNauOtxNPZVmQtNxUHD7U8gOJxB3PrJbieYu3pXLBm/P4b3MqwX7efHBlf07qFF1yJ1cxv0XvmvtOo0xisoiINGhVHpY6dIFMy7LIzMwkMDCQjz/+uFobJx4kbTv8eDtsnAUx3eCKn8EvxLzmKIRVB3K5ul1wVB+TkpHHUz+vdefXhAf58sEV/enWvIw1nlzBjaPgwGefd1SfLSIi9UOVg5sXXnihRHBjt9tp0qQJAwYMoHFj1QaRQzidpmfkt4ehIMtsS14BX1wGF38BXj6weQ7k7IXASGh9whF9TEGRk6lzt/DK7A1kFziw2eDCPnH836kdiAwupzcmqlPx46AmEH/8EX22iIjUL1UObi6//PIaaIZ4pH2b4PsJkPiveR43APpfC9Nvhk2/ww+3wdmvFg9JdT338NWFy/DX+j08PH0Vm/dmA9CrRSMeHtWFHnGNKj4wukvx486jj+izRUSk/qnyb/P33nuP4OBgLrig5PDBl19+SU5ODuPHj6+2xkkDZlnwyQWQugl8gmDEw9DvarDbTdLwZ2Mh4WMIioC1M8wx3S6s0kfsTMvlsR9WM3NVMgCRwX7cc1pHzu3VDLu9Eqt5B0dDSCxkJh31cJiIiNQfVU4onjx5MpGRkaW2R0VF8eSTT1ZLo8QD7FxsAhvfYLhxHgy41gQ2AB1OhTOeN4/nvmRW9G4cD837VuqtnU6L1+ds5KTn5zBzVTJedhtXDmnF73cO4/w+zSsX2IBZdHPMx3DB+9BiQJW/ooiI1E9V7rlJTEykVatWpba3bNmSxMTEammUeIDV35n79iOhccvSr/e9EtJ3wN8HgpxuF5hg4zDyCh3c8cUyZqxIAqB/q3AePbsLHWMquUzDoZr3rXRQJSIiDUOVg5uoqCiWL19OfHx8ie3Lli0jIiKiutolDZllwerp5nHn0kUf3YY/APmZsGGWWc37MPZl5XPNh4tYkpiGj5eNx87uyph+cRUuByIiIseeKgc3Y8eO5ZZbbiEkJITjjzezS/78809uvfVWLrroompvoDRASctMQT6fQLOUQnlsNjj92Uq95aY9WVzx3kISU3MI9ffmf5f2ZVAbBdMiIlJalYObxx57jK1bt3LSSSfh7W0OdzqdXHbZZcq5EWP19+a+3cnVVJBvP1e+v5D03ELiwgN47/L+tI0KPur3FRERz1Tl4MbX15fPP/+cxx9/nISEBAICAujWrRstW5aRVyHHHssqzrfpdNZRv92utFyu/XAR6bmF9IxrxDvj+5Zft0ZERIQjCG5c2rVrR7t27aqzLeIJUlZB6mbw8jPJxEchv8jBDZ8sYV92AZ1jQ5l2zUACfL2qqaEiIuKpqjwV/LzzzuPpp58utf2ZZ54pVftGjkGuIam2I4qXVzhCj/6wmmXb0wgL8OHNS/oosBERkUqpcnDz119/cfrppVdtPu200/jrr7+qpVHSgLmCm4pmSVXCl4u288n8RGw2ePGinrSIOPrcHREROTZUObjJysrC19e31HYfHx8yMjKqpVHSQO1eC3vXgd3HFOo7Qit3pnP/dysBuO2k9pzYIaq6WigiIseAKgc33bp14/PPPy+1/bPPPqNz587V0ihpoFy9Nm2Gg38Zq3BXQnpOITd8spj8IicndmjCzcPbVmMDRUTkWFDlhOIHHniAc889l02bNjF8+HAAZs+ezaeffspXX31V7Q2UBuQoh6Qsy+Keb5azPTWXuPAAXhzTq/JLKYiIiBxQ5eBm1KhRfPfddzz55JN89dVXBAQE0KNHD37//XfCw8Nroo1S3xUVwJa/YPcqsHtDh9OO6G0+XZDIzyuT8fGy8drFvQkL9KnmhoqIyLHgiKaCn3HGGZxxxhkAZGRkMG3aNO68804WL16Mw+Go1gZKPbVrKaz6DrYvgF1LoCjPbG91PARWPchdl5zJoz+sBuCukR3p3rxR9bVVRESOKUdc5+avv/7i3Xff5euvv6Zp06ace+65vPbaa9XZNqmvCnPh/VFQkFm8LaAxxA0w60VVUV6hg5unLSG/yMmw9k24amjphVlFREQqq0rBTXJyMu+//z7vvvsuGRkZXHjhheTn5/Pdd98pmfhYsnOxCWwCwuGUx0xQE9G2Uqt6l+WxH1ezPiWLyGA/nr+wh/JsRETkqFR6ttSoUaPo0KEDy5cv58UXX2TXrl288sorNdk2qa+2zTP3rYdBr0sgst0RBzbTl+3ik/mJALwwpoeWVhARkaNW6Z6bn3/+mVtuuYUbbrhByy4c6xL/NfctBh/xW+QXOXjul3W8/fcWAK4f1obj2jWpjtaJiMgxrtI9N//88w+ZmZn06dOHAQMG8Oqrr7J3796abJvUR44ik0QM0HLQEb3Fxt1ZnPPav+7A5pKBLbjjlPbV1UIRETnGVTq4GThwIG+//TZJSUlcd911fPbZZzRt2hSn08msWbPIzMw8/JtIw5eyAgqywC8MoqqWZ2VZFp/M38aZr/zN6qQMwoN8eeeyvjw+uhs+XlWuJykiIlKmKl9RgoKCuPLKK/nnn39YsWIFd9xxB0899RRRUVGcddZZNdFGqU1OJ3x1Fcy8t+zXXfk2LQaAvWoLWX62cDv3fbuSvEInx7WLZOatxzGic/RRNlhERKSko/pzuUOHDjzzzDPs2LGDadOmVVebpC7t2wArv4L/XoM960u/vm2uuW9ZtXybHftzePxHU8dmwolt+eCK/kSF+h9ta0VEREqplrEALy8vRo8ezfTp04/o+Ndee434+Hj8/f0ZMGAACxYsKHffE044AZvNVurmKiooRykrpfjx8s9KvmZZkPifeVyFZGLLsrj76+VkFzjoF9+YiSe313RvERGpMXWe6PD5558zceJEHnroIZYsWUKPHj0YOXIku3fvLnP/b775hqSkJPdt5cqVeHl5ccEFF9Ryyz1U1kHnffkXZpjKZe8GyNkL3v7QtFel3/KT+YnM3bgPfx87z56vOjYiIlKz6jy4mTJlCtdccw1XXHEFnTt35s033yQwMJCpU6eWuX94eDgxMTHu26xZswgMDFRwU10yk4sfp28vnvYNxY+b9QVv30q93fbUHCb/tAYwyyrERwZVV0tFRETKVKfBTUFBAYsXL2bEiBHubXa7nREjRjBv3rxKvce7777LRRddRFBQ2RfN/Px8MjIyStykAgcPSwEs/7z4sSuZuJJTwJ3O4uGo/vHhXD44vnraKCIiUoE6DW727t2Lw+EgOrrkjJno6GiSk5PLOarYggULWLlyJVdffXW5+0yePJmwsDD3LS4u7qjb7dFcw1LtTjH3q76HwgOLYrqL91UuuPlkQSL/bjLDUc+c313DUSIiUivqfFjqaLz77rt069aN/v37l7vPpEmTSE9Pd9+2b99eiy1sgLIOBJWdR0NYHOSnw/qfIX0HpCWCzQ5x5Z9vl817snhyhhmOuvtUDUeJiEjtqdPgJjIyEi8vL1JSSg6FpKSkEBMTU+Gx2dnZfPbZZ1x11VUV7ufn50doaGiJm1TA1XMTGgvdDuQxLfu8eEgqpjv4hVT4FoUOJ7d9nkBuoYNBrSMYPyi+5torIiJyiDoNbnx9fenTpw+zZ892b3M6ncyePZtBgyoe+vjyyy/Jz8/nkksuqelmHltcCcXB0dDjIvN44yxY+4N5XIn6Ni/+tp7lO9IJC/BhyhjNjhIRkdpV58NSEydO5O233+aDDz5gzZo13HDDDWRnZ3PFFVcAcNlllzFp0qRSx7377ruMHj2aiIiI2m6y5yoqgNxU8zg4Gpp0gNie4CyC1d+b7YcJbuZv3sfrczYB8NS53YgNC6jBBouIiJRW6VXBa8qYMWPYs2cPDz74IMnJyfTs2ZOZM2e6k4wTExOx20vGYOvWreOff/7h119/rYsme67sPebe7g0B4eZx9zGQlFC8TwXJxOm5hdz+eQKWBRf0ac5p3WJrrq0iIiLlsFmWZdV1I2pTRkYGYWFhpKenK//mUDsXw9vDIaQp3GGSgcnaDc93BMsBke1hwsIyD7Usi1s+S+CHZbtoGRHIjFuOI9ivzmNnERHxEFW5ftf5sJTUI65k4pCDpuYHR0Gb4eZxBb02b/21mR+W7cLLbuPFMT0V2IiISJ3RFUiKuQr4BR+yUvcpj4FfMAy9rdQhlmXx7C/r3Hk2d5zSnl4tGtdwQ0VERMqn4EaKZbqCm6iS26M6wQXvl9rd4bS4/7uVTFuQCMBdp3bghmFtariRIiIiFVNwI8XcPTcV1xgCyC9yMPHzZcxYkYTNBk+M7sbFA1rUcANFREQOT8GNFMsqp+fmEJZlcePHS5i9djc+XjZeHNOLM7prZpSIiNQPCm6kmCu4Cam452bGiiRmr92Nn7edty/ry/Htm9RC40RERCpHs6WkWHkJxQfJL3Lw9My1ANx4QlsFNiIiUu8ouBHDsspPKD7IB/9uZXtqLtGhflxzfKtaapyIiEjlKbgRIy8dHPnmcTk9N/uzC3jl940A3HFKBwJ9NaopIiL1j4IbMVwF/PzCwKfs9aBemr2BzLwiOsWGcl7v5rXYOBERkcpTcCNG1oHVwEPK7rXZsjebj//bBsD9Z3TCSyt9i4hIPaXg5lhSmAff3wRLPyn9mqvnppwhqad+XkOR0+LEDk0Y0jayBhspIiJydBTcHEtWfw9LP4Zf7zMJxAeroMbNgi2p/LIqBS+7jXtP71QLDRURETlyCm6OJWumm/vc/ZC+o+RrmQeGpQ6pTux0Wjw+YzUAY/rF0S46pKZbKSIiclQU3BwrCrJh42/Fz5OXl3zdPSxVsudm+rJdLN+RTrCfNxNPbl/DjRQRETl6Cm6OFRt/g6K84udJhwY3rp6b4pybvEIHzxwo2HfDCW2IDPar6VaKiIgcNQU3x4o1P5j7gHBzX17PzUGzpd79Zwu70vNo1iiAq4aqYJ+IiDQMCm6OBUX5sP4X83jIreY+aVnJfQ5ZemFvVj5vzNkEwP+N7IC/j1dttFREROSoKbg5Fmz5C/IzTLJw3yvMtoydkL3PPC4qgJwDjw8kFL8waz1Z+UV0bx7GWT2a1kGjRUREjoyCm2OBa5ZUpzPBPwzCW5vnyQd6b7L3mHu7NwQ0ZkNKJtMWJAJw/xmdsatgn4iINCAKbjydowjWzjCPO40y9zHdzb0rqdg1JBUUBXY7T/60BqcFI7tE079VeO22V0RE5CgpuPF0ifPMkFNAY2g5xGyLPRDcJB8S3IREs3hbKn+s24O33cY9p6lgn4iINDwKbjyda5ZUh9PBy8c8julh7g/tuQmO5rU/TBLx+X2a0yoyqBYbKiIiUj0U3Hgyp7M4uOl0VvF2V8/Nvo2Qn+WeBr7fHs7va3djt8F1w9rUcmNFRESqh4IbT7ZrKWTuAt9gaH1C8fbgKAiJBSxIWeVeeuG/Pd4AnNG9qXptRESkwVJw48lcs6TanQI+/iVfcycVL3MPS81LNrVsblCvjYiINGAKbjxVYR4s+8w8ds2SOpg7qbg4uEmxGjG8YxSdm4bWUiNFRESqn4IbT7X0I7NeVFgcdDyz9OsHTQcvyjDDUnusMG48Qb02IiLSsHnXdQOkBhTlwz8vmMdDbwNv39L7uHpudq8ByxTpi20eT9941bUREZGGTT03nijhU7O8Qkgs9Lyk7H0atTTVip2FeFsFAIw5oU8tNlJERKRmKLhpqHYtheVfmOneB3MUwj9TzOMht5ZOJHax2YqHpoAsWxDHdY6rocaKiIjUHgU3DdUX4+Gba+D7m8wSCy7Lv4C0RAhqAr3HV/gWBU26FD8JjsZm0xpSIiLS8Cm4aYjyMyFtm3m87FP44lIozAWnA/5+3mwffDP4Blb4Nn9lNnM/DgrXyt8iIuIZFNw0RPs2mnufQPDyg3U/wcfnw+L3IHUTBIRD36sqfIvcAgf/Wx/sfm4Lia7JFouIiNQaBTcN0T6z/hOxPeDSb8AvFLb9AzPuMNsH3Qh+weUfD0xbkMiSnEjyOTCTKjimBhssIiJSexTcNESunpuINhA/FC7/EQIjzTb/MOh/bYWH5xc5eOuvzTjwIjOsg9kYHFWDDRYREak9Cm4aIndw087cx/aAK3+BLufA6DdMgFOBrxfvJDkjj5hQf8IGXQqBESXXnhIREWnAVMSvIdq7wdxHtC3eFtkWLnj/sIcWOZy88acJjq49vjU+A0+CAdeaqeEiIiIeQD03DY1lFefcHBzcVNL0ZbvYnppLRJAvY/u3MBsV2IiIiAdRcNPQZO2Ggkyw2SG8VZUOdTotXvvD9NpcdVwrAny9aqKFIiIidUrBTUOz78CQVKMW4O1XpUN/W5PCpj3ZhPp7c+nAljXQOBERkbqn4KahcScTV31IaurcLQCMG9iSEH+f6myViIhIvaHgpqE5dKZUJa1JyuC/zal42W3qtREREY+m4KahcScTt6nSYe8d6LU5tWsMTRsFVHerRERE6g0FNw1NWdPAD2NfVj7fJewC4Moh8TXQKBERkfpDwU1D4iiC/aYHpirBzafzEykoctK9eRi9WzSuocaJiIjUDwpuGpK0beAsAu8ACG12+P2BgiInH/1nVhC/ckgrbKppIyIiHk7BTUNycL6NvXI/up9WJLE7M5+oED9O7xZbg40TERGpHxTcNCSuGjeVTCa2LMudSHzJwJb4euvHLSIink9Xu4akitPAlySmsWxHOr5edi4e0KIGGyYiIlJ/KLhpSKpYwO/9f7cCcFbPpkQGV62asYiISEOl4KYh2Vv54CYtp4BfViYDcPng+BpslIiISP2i4KahyM+CTFOrpjI5N9OX7aLA4aRTbChdm4XVcONERETqDwU3DUXqZnMfGAGB4Yfd/ctFOwA4v0/zmmyViIhIvaPgpqGoQr7N2uQMVuxMx9tuY3TPpjXcMBERkfpFwU1DUYWZUl8d6LU5qVMUEUokFhGRY4yCm4bCHdxUnG9T6HDyXcJOAM7vE1fTrRIREal3FNw0FJUclpqzbg97swqIDPblhA5NaqFhIiIi9YuCm4bAsio9DfyrxdsBGN2zGT5e+vGKiMixR1e/hiB7L+SnAzYIb13ubvuy8pm9ZjcA5/fVLCkRETk2KbhpCFxDUo3iwMe/3N2+S9hFkdOiW7MwOsaE1lLjRERE6hcFNw2Be8HMww1JmVlSF6jXRkREjmEKbhqCHQvNfXSXcndZtSudNUkZ+HrZOauHatuIiMixq86Dm9dee434+Hj8/f0ZMGAACxYsqHD/tLQ0brrpJmJjY/Hz86N9+/b89NNPtdTaOrLlb3Mff3y5u/y0IgmA4R2jaBToWxutEhERqZe86/LDP//8cyZOnMibb77JgAEDePHFFxk5ciTr1q0jKiqq1P4FBQWcfPLJREVF8dVXX9GsWTO2bdtGo0aNar/xtSV9B+zfAjY7tBhY7m6/rkoB4NSuMbXVMhERkXqpToObKVOmcM0113DFFVcA8OabbzJjxgymTp3KPffcU2r/qVOnkpqayr///ouPjw8A8fHxtdnk2rd1rrmP7Qn+ZScJb96TxYbdWXjbbZzYsXRQKCIiciyps2GpgoICFi9ezIgRI4obY7czYsQI5s2bV+Yx06dPZ9CgQdx0001ER0fTtWtXnnzySRwOR7mfk5+fT0ZGRolbg7L1L3Pf6rhyd/l1tem1GdQmgrAAn9polYiISL1VZ8HN3r17cTgcREdHl9geHR1NcnJymcds3ryZr776CofDwU8//cQDDzzA888/z+OPP17u50yePJmwsDD3LS6ugS1JsPUfcx9fQXCzypyvU7poSEpERKTOE4qrwul0EhUVxVtvvUWfPn0YM2YM9913H2+++Wa5x0yaNIn09HT3bfv27bXY4qOUth32bwWbV7n5Nrsz8liSmAbAyZ2iy9xHRETkWFJnOTeRkZF4eXmRkpJSYntKSgoxMWX3QMTGxuLj44OXl5d7W6dOnUhOTqagoABf39KzhPz8/PDza6ArY7t6bZr2Ar+QMneZtcacvx5xjYgJK7/An4iIyLGiznpufH196dOnD7Nnz3ZvczqdzJ49m0GDBpV5zJAhQ9i4cSNOp9O9bf369cTGxpYZ2DR47iGpoeXu8suBWVIju6jXRkREBOp4WGrixIm8/fbbfPDBB6xZs4YbbriB7Oxs9+ypyy67jEmTJrn3v+GGG0hNTeXWW29l/fr1zJgxgyeffJKbbrqprr5CzXIlE5eTb5ORV8i8TXsBOKWz8m1ERESgjqeCjxkzhj179vDggw+SnJxMz549mTlzpjvJODExEbu9OP6Ki4vjl19+4fbbb6d79+40a9aMW2+9lbvvvruuvkLN2b8N0hIrzLeZs24PhQ6LNk2CaBsVXMsNFBERqZ/qNLgBmDBhAhMmTCjztTlz5pTaNmjQIP77778ablU9sO1AfZtmvcGv7MDlF82SEhERKaVBzZY6priXXCg73ya/yMGctbsBOKWz8m1ERERcFNzUV4epb/Pvpn1kFziIDvWjR/NGtdcuERGRek7BTX20fyukJ4LdG+IGlLmLq3DfyZ2jsdtttdg4ERGR+k3BTX3krm9Tdr6NZVnMXuMaklK+jYiIyMEU3NRHruCmnPWkNu7OYndmPn7edvq3Cq/FhomIiNR/Cm7qG8s6bDLxv5v2AdAvPhx/H68y9xERETlWKbipb/ZthIwd4OVbbr7NvwcK9w1qE1GbLRMREWkQFNzUNxsPLEfRYiD4BpV62eG0mHeg52awghsREZFSFNzUN5sOBDdtTirz5dW7MsjIKyLEz5tuzcJqsWEiIiINg4Kb+qQovziZuG3ZwY1rSGpA63C8vfTjExEROZSujvVJ4jwozIHgaIjuWuYucw8MSQ1qE1mbLRMREWkwFNzUJ658mzbDwVa6MF9BkZOFW1IBGNJW+TYiIiJlUXBTn2z63dy3HVHmy8t2pJFb6CAiyJf2USG12DAREZGGo85XBZcDMpMhZSVgg9YnlrnL3I0m32ZgmwgtuSAiDYrD4aCwsLCumyH1nI+PD15eR1+/TcFNfeHqtWnaE4LKHnJyFe8bonwbEWlAsrKy2LFjB5Zl1XVTpJ6z2Ww0b96c4ODSSw9VhYKb+mJjxVPAcwscLE3cD6i+jYg0HA6Hgx07dhAYGEiTJk2wlZFPKAJm3cQ9e/awY8cO2rVrd1Q9OApu6gOn46B8m7KDm4VbUyl0WDQN86dlRGAtNk5E5MgVFhZiWRZNmjQhICCgrpsj9VyTJk3YunUrhYWFRxXcKKG4PkhKgNxU8A2B5v3K3MU1JDW4baT+8hGRBke/t6QyquvfiYKb+mDjgV6b1sPAy6fMXeYdKN6nISkREZGKKbipDzYdVN+mDOm5hazYmQ7AYCUTi4iIVEjBTV3LS4ftC8zjcvJt5m/eh9OC1k2CiAnzr8XGiYiINDwKburalr/AckBEW2gcX+Yu8zYfWHKhtYakREREDkfBTV1bP9PclzMFHGD+ZrPkwiDl24iIHLNUBLHyFNzUpaJ8WPODedz5rDJ3ScspYE1yBgD9W4XXVstERGqEZVnkFBTVya2qRQRnzpzJ0KFDadSoEREREZx55pls2rTJ/fqOHTsYO3Ys4eHhBAUF0bdvX+bPn+9+/YcffqBfv374+/sTGRnJOeec437NZrPx3Xfflfi8Ro0a8f777wOwdetWbDYbn3/+OcOGDcPf359PPvmEffv2MXbsWJo1a0ZgYCDdunVj2rRpJd7H6XTyzDPP0LZtW/z8/GjRogVPPPEEAMOHD2fChAkl9t+zZw++vr7Mnj27SuenPlOdm7q0cbbJuQmJhRaDytxlwZZULAvaNAkiKkT5NiLSsOUWOuj84C918tmrHx1JoG/lL3vZ2dlMnDiR7t27k5WVxYMPPsg555xDQkICOTk5DBs2jGbNmjF9+nRiYmJYsmQJTqcTgBkzZnDOOedw33338eGHH1JQUMBPP/1U5Tbfc889PP/88/Tq1Qt/f3/y8vLo06cPd999N6GhocyYMYNLL72UNm3a0L9/fwAmTZrE22+/zQsvvMDQoUNJSkpi7dq1AFx99dVMmDCB559/Hj8/PwA+/vhjmjVrxvDhZU9qaYgU3NSllV+b+y7ngr3sYkXzD6wCPlD5NiIiteq8884r8Xzq1Kk0adKE1atX8++//7Jnzx4WLlxIeLjpVW/btq173yeeeIKLLrqIRx55xL2tR48eVW7Dbbfdxrnnnlti25133ul+fPPNN/PLL7/wxRdf0L9/fzIzM3nppZd49dVXGT9+PABt2rRh6NChAJx77rlMmDCB77//ngsvvBCA999/n8svv9yjahEpuKkrBdmw7kAU3/W8cnf770Ay8QAFNyLiAQJ8vFj96Mg6++yq2LBhAw8++CDz589n79697l6ZxMREEhIS6NWrlzuwOVRCQgLXXHPNUbe5b9++JZ47HA6efPJJvvjiC3bu3ElBQQH5+fkEBprK9WvWrCE/P5+TTio7j9Pf359LL72UqVOncuGFF7JkyRJWrlzJ9OnTj7qt9YmCm7qy7mcozIHGraBZ7zJ3Sc8pZHWSybcZqHwbEfEANputSkNDdWnUqFG0bNmSt99+m6ZNm+J0OunatSsFBQWHXUricK/bbLZSOUBlJQwHBQWVeP7ss8/y0ksv8eKLL9KtWzeCgoK47bbbKCgoqNTnghma6tmzJzt27OC9995j+PDhtGzZ8rDHNSRKKK4rK78x913Pg3K6AhdsNfk2rZsEERWqfBsRkdqyb98+1q1bx/33389JJ51Ep06d2L9/v/v17t27k5CQQGpqapnHd+/evcIE3SZNmpCUlOR+vmHDBnJycg7brrlz53L22WdzySWX0KNHD1q3bs369evdr7dr146AgIAKP7tbt2707duXt99+m08//ZQrr7zysJ/b0Ci4qQu5abBxlnlcwZDUfNeQVCsNSYmI1KbGjRsTERHBW2+9xcaNG/n999+ZOHGi+/WxY8cSExPD6NGjmTt3Lps3b+brr79m3rx5ADz00ENMmzaNhx56iDVr1rBixQqefvpp9/HDhw/n1VdfZenSpSxatIjrr78eH5+yl985WLt27Zg1axb//vsva9as4brrriMlJcX9ur+/P3fffTd33XUXH374IZs2beK///7j3XffLfE+V199NU899RSWZZWYxeUpFNzUhbU/gqMAojpDdOdyd/tviwluBrbWkJSISG2y2+189tlnLF68mK5du3L77bfz7LPPul/39fXl119/JSoqitNPP51u3brx1FNPuVeyPuGEE/jyyy+ZPn06PXv2ZPjw4SxYsMB9/PPPP09cXBzHHXccF198MXfeeac7b6Yi999/P71792bkyJGccMIJ7gDrYA888AB33HEHDz74IJ06dWLMmDHs3r27xD5jx47F29ubsWPH4u/veSMDNquqE/8buIyMDMLCwkhPTyc0NLRuGvHhaNj8Bwy/H47/vzJ3Sc8tpOejv2JZMP/ek4jWsJSINEB5eXls2bKFVq1aeeRFtKHaunUrbdq0YeHChfTuXXbeZ12o6N9LVa7fDSOry5Nk7YYtf5rHFQxJLTqQb9MqMkiBjYiIVIvCwkL27dvH/fffz8CBA+tVYFOdNCxV21Z/D5YTmvaG8Nbl7uaaAq4hKRERqS5z584lNjaWhQsX8uabb9Z1c2qMem5q24qvzH238yvc7b/NKt4nIiLV64QTTqjyMhQNkXpualP2Xtj+H2CDLuVnp2fkFbJqVzqgmVIiIiJVpeCmNu07sOBaWHMIbVrubou2puK0ID4ikJgw5duIiIhUhYKb2pS2zdw3qrgSpIakREREjpyCm9rkCm4aVxzcuIv3KZlYRESkyhTc1Kb9rp6bFuXuklfoYMVOk2/TX/k2IiIiVabgpjZVYlhqQ0oWTgvCg3xpqnwbERGRKlNwU5vSEs19BcNSa5PNKuAdokOwlbOgpoiI1H/x8fG8+OKLdd2MY5KCm9ridED6DvO4gmGpdcmZAHSICamNVomIiHgcBTe1JWMnOIvA7gMhseXutvZAcNNRwY2IiNQRh8OB0+ms62YcMQU3tcU1JNUoDuxe5e62Vj03IuLJLAsKsuvmVoXKvG+99RZNmzYtdYE/++yzufLKK9m0aRNnn3020dHRBAcH069fP3777bcjPi1TpkyhW7duBAUFERcXx4033khWVlaJfebOncsJJ5xAYGAgjRs3ZuTIkezfvx8Ap9PJM888Q9u2bfHz86NFixY88cQTAMyZMwebzUZaWpr7vRISErDZbGzduhWA999/n0aNGjF9+nQ6d+6Mn58fiYmJLFy4kJNPPpnIyEjCwsIYNmwYS5YsKdGutLQ0rrvuOqKjo/H396dr1678+OOPZGdnExoayldffVVi/++++46goCAyMzOP+HwdjpZfqC2VmCm1LyufvVn5ALSPVnAjIh6oMAeeLL+IaY26dxf4BlVq1wsuuICbb76ZP/74g5NOOgmA1NRUZs6cyU8//URWVhann346TzzxBH5+fnz44YeMGjWKdevW0aJF+b/ny2O323n55Zdp1aoVmzdv5sYbb+Suu+7i9ddfB0wwctJJJ3HllVfy0ksv4e3tzR9//IHD4QBg0qRJvP3227zwwgsMHTqUpKQk1q5dW6U25OTk8PTTT/POO+8QERFBVFQUmzdvZvz48bzyyitYlsXzzz/P6aefzoYNGwgJCcHpdHLaaaeRmZnJxx9/TJs2bVi9ejVeXl4EBQVx0UUX8d5773H++cVLDrmeh4TU3HVOwU1tqcRMKVe+TcuIQIL89KMREakrjRs35rTTTuPTTz91BzdfffUVkZGRnHjiidjtdnr06OHe/7HHHuPbb79l+vTpTJgwocqfd9ttt7kfx8fH8/jjj3P99de7g5tnnnmGvn37up8DdOnSBYDMzExeeuklXn31VcaPHw9AmzZtGDp0aJXaUFhYyOuvv17iew0fPrzEPm+99RaNGjXizz//5Mwzz+S3335jwYIFrFmzhvbt2wPQunXxotBXX301gwcPJikpidjYWHbv3s1PP/10VL1claEraG1xD0uVH9G7h6TUayMinson0PSg1NVnV8G4ceO45ppreP311/Hz8+OTTz7hoosuwm63k5WVxcMPP8yMGTNISkqiqKiI3NxcEhMTj6hpv/32G5MnT2bt2rVkZGRQVFREXl4eOTk5BAYGkpCQwAUXXFDmsWvWrCE/P98dhB0pX19funfvXmJbSkoK999/P3PmzGH37t04HA5ycnLc3zMhIYHmzZu7A5tD9e/fny5duvDBBx9wzz338PHHH9OyZUuOP/74o2rr4Sjnpra4hqUax5e7yzolE4uIp7PZzNBQXdyqWF5j1KhRWJbFjBkz2L59O3///Tfjxo0D4M477+Tbb7/lySef5O+//yYhIYFu3bpRUFBQ5VOydetWzjzzTLp3787XX3/N4sWLee211wDc7xcQEFDu8RW9BmbICyixGnhhYWGZ73NoCZLx48eTkJDASy+9xL///ktCQgIRERGVapfL1Vdfzfvvvw+YIakrrriixkudKLipLe6em0rUuIkJrY0WiYhIBfz9/Tn33HP55JNPmDZtGh06dKB3796ASe69/PLLOeecc+jWrRsxMTHu5NyqWrx4MU6nk+eff56BAwfSvn17du0q2bvVvXt3Zs+eXebx7dq1IyAgoNzXmzRpAkBSUpJ7W0JCQqXaNnfuXG655RZOP/10unTpgp+fH3v37i3Rrh07drB+/fpy3+OSSy5h27ZtvPzyy6xevdo9dFaTFNzUhqICMxUcyh2Wcjot1qeYzHjNlBIRqR/GjRvHjBkzmDp1qrvXBkxA8c0335CQkMCyZcu4+OKLj3jqdNu2bSksLOSVV15h8+bNfPTRR7z55psl9pk0aRILFy7kxhtvZPny5axdu5Y33niDvXv34u/vz913381dd93Fhx9+yKZNm/jvv/9499133e8fFxfHww8/zIYNG5gxYwbPP/98pdrWrl07PvroI9asWcP8+fMZN25cid6aYcOGcfzxx3Peeecxa9YstmzZws8//8zMmTPd+zRu3Jhzzz2X//u//+OUU06hefPmR3SeqkLBTW1I3w5Y4B0AwVFl7pKYmkNuoQNfbzvxEVUbFxYRkZoxfPhwwsPDWbduHRdffLF7+5QpU2jcuDGDBw9m1KhRjBw50t2rU1U9evRgypQpPP3003Tt2pVPPvmEyZMnl9inffv2/Prrryxbtoz+/fszaNAgvv/+e7y9TersAw88wB133MGDDz5Ip06dGDNmDLt37wbAx8eHadOmsXbtWrp3787TTz/N448/Xqm2vfvuu+zfv5/evXtz6aWXcssttxAVVfI69vXXX9OvXz/Gjh1L586dueuuu9yzuFyuuuoqCgoKuPLKK4/oHFWVzbKqMPHfA2RkZBAWFkZ6ejqhobU0/LPpD/hoNER2gAkLytxl5spkrv94MV2bhfLjzcfVTrtERGpYXl4eW7ZsoVWrVvj7a728Y9VHH33E7bffzq5du/D19S13v4r+vVTl+q3ZUrUh7fA1btzLLkQr30ZERDxDTk4OSUlJPPXUU1x33XUVBjbVScNStcE9U6qCGjcpJplYM6VERDzLJ598QnBwcJk3V60aT/XMM8/QsWNHYmJimDRpUq19rnpuakNlZkoladkFERFPdNZZZzFgwIAyX/Px8anl1tSuhx9+mIcffrjWP1fBTW04zLBUXqGDrfuyAfXciIh4mpCQkBpdakBK07BUbTjMsNSGlCycFjQO9KFJiF8tNkxEpHYcY3NX5AhV178TBTc1rTAXss10vPKGpVzF+zrGhNZ41UYRkdrk5eUFcESVe+XY4/p34vp3c6Q0LFXTXPk2viEQ0LjMXdwzpTQkJSIextvbm8DAQPbs2YOPj497KQCRQzmdTvbs2UNgYKC7fs+RUnBT0w4ekiqnV2at1pQSEQ9ls9mIjY1ly5YtbNu2ra6bI/Wc3W6nRYsWRz2KoeCmprmTiStaU0o9NyLiuXx9fWnXrp2GpuSwfH19q6V3T8FNTTvMTKl9WfnszcoHoH20ghsR8Ux2u10ViqXW1IvBz9dee434+Hj8/f0ZMGAACxaUvUQBwPvvv4/NZitxq9f/YQ4zU8qVb9MyIpAgP8WaIiIiR6vOg5vPP/+ciRMn8tBDD7FkyRJ69OjByJEj3Qt+lSU0NJSkpCT3rV6P4x6mgJ97SEq9NiIiItWizoObKVOmcM0113DFFVfQuXNn3nzzTQIDA5k6dWq5x9hsNmJiYty36OjoWmxxFR1mWGp9ivJtREREqlOdjoMUFBSwePHiEutN2O12RowYwbx588o9Lisri5YtW+J0OunduzdPPvlkuetz5Ofnk5+f736enp4OmNVFa1xeBqSlmsf2RlDGZ67eloIzP4dmQbXUJhERkQbIdY2sTKG/Og1u9u7di8PhKNXzEh0dzdq1a8s8pkOHDkydOpXu3buTnp7Oc889x+DBg1m1ahXNmzcvtf/kyZN55JFHSm2Pi4urni9RWU9V/HljX6ydZoiIiDRkmZmZhIWFVbhPg8tgHTRoEIMGDXI/Hzx4MJ06deJ///sfjz32WKn9J02axMSJE93PnU4nqampREREVHs14IyMDOLi4ti+fTuhoaHV+t5Sks517dG5rj0617VH57r2VNe5tiyLzMxMmjZteth96zS4iYyMxMvLi5SUlBLbU1JSiImJqdR7+Pj40KtXLzZu3Fjm635+fvj5lVyvqVGjRkfU3soKDQ3Vf5ZaonNde3Sua4/Ode3Rua491XGuD9dj41KnCcW+vr706dOH2bNnu7c5nU5mz55donemIg6HgxUrVhAbG1tTzRQREZEGpM6HpSZOnMj48ePp27cv/fv358UXXyQ7O5srrrgCgMsuu4xmzZoxefJkAB599FEGDhxI27ZtSUtL49lnn2Xbtm1cffXVdfk1REREpJ6o8+BmzJgx7NmzhwcffJDk5GR69uzJzJkz3UnGiYmJJUox79+/n2uuuYbk5GQaN25Mnz59+Pfff+ncuXNdfQU3Pz8/HnrooVLDYFL9dK5rj8517dG5rj0617WnLs61zarMnCoRERGRBqLOi/iJiIiIVCcFNyIiIuJRFNyIiIiIR1FwIyIiIh5FwU01ee2114iPj8ff358BAwawYMGCum5Sgzd58mT69etHSEgIUVFRjB49mnXr1pXYJy8vj5tuuomIiAiCg4M577zzShWFlKp76qmnsNls3Hbbbe5tOtfVZ+fOnVxyySVEREQQEBBAt27dWLRokft1y7J48MEHiY2NJSAggBEjRrBhw4Y6bHHD5HA4eOCBB2jVqhUBAQG0adOGxx57rMTaRDrXR+6vv/5i1KhRNG3aFJvNxnfffVfi9cqc29TUVMaNG0doaCiNGjXiqquuIisr6+gbZ8lR++yzzyxfX19r6tSp1qpVq6xrrrnGatSokZWSklLXTWvQRo4cab333nvWypUrrYSEBOv000+3WrRoYWVlZbn3uf766624uDhr9uzZ1qJFi6yBAwdagwcPrsNWN3wLFiyw4uPjre7du1u33nqre7vOdfVITU21WrZsaV1++eXW/Pnzrc2bN1u//PKLtXHjRvc+Tz31lBUWFmZ999131rJly6yzzjrLatWqlZWbm1uHLW94nnjiCSsiIsL68ccfrS1btlhffvmlFRwcbL300kvufXSuj9xPP/1k3XfffdY333xjAda3335b4vXKnNtTTz3V6tGjh/Xff/9Zf//9t9W2bVtr7NixR902BTfVoH///tZNN93kfu5wOKymTZtakydPrsNWeZ7du3dbgPXnn39almVZaWlplo+Pj/Xll1+691mzZo0FWPPmzaurZjZomZmZVrt27axZs2ZZw4YNcwc3OtfV5+6777aGDh1a7utOp9OKiYmxnn32Wfe2tLQ0y8/Pz5o2bVptNNFjnHHGGdaVV15ZYtu5555rjRs3zrIsnevqdGhwU5lzu3r1aguwFi5c6N7n559/tmw2m7Vz586jao+GpY5SQUEBixcvZsSIEe5tdrudESNGMG/evDpsmedJT08HIDw8HIDFixdTWFhY4tx37NiRFi1a6NwfoZtuuokzzjijxDkFnevqNH36dPr27csFF1xAVFQUvXr14u2333a/vmXLFpKTk0uc67CwMAYMGKBzXUWDBw9m9uzZrF+/HoBly5bxzz//cNpppwE61zWpMud23rx5NGrUiL59+7r3GTFiBHa7nfnz5x/V59d5heKGbu/evTgcDndFZZfo6GjWrl1bR63yPE6nk9tuu40hQ4bQtWtXAJKTk/H19S21EGp0dDTJycl10MqG7bPPPmPJkiUsXLiw1Gs619Vn8+bNvPHGG0ycOJF7772XhQsXcsstt+Dr68v48ePd57Os3yk611Vzzz33kJGRQceOHfHy8sLhcPDEE08wbtw4AJ3rGlSZc5ucnExUVFSJ1729vQkPDz/q86/gRhqEm266iZUrV/LPP//UdVM80vbt27n11luZNWsW/v7+dd0cj+Z0Ounbty9PPvkkAL169WLlypW8+eabjB8/vo5b51m++OILPvnkEz799FO6dOlCQkICt912G02bNtW59nAaljpKkZGReHl5lZo1kpKSQkxMTB21yrNMmDCBH3/8kT/++IPmzZu7t8fExFBQUEBaWlqJ/XXuq27x4sXs3r2b3r174+3tjbe3N3/++Scvv/wy3t7eREdH61xXk9jY2FJr4XXq1InExEQA9/nU75Sj93//93/cc889XHTRRXTr1o1LL72U22+/3b0Qs851zanMuY2JiWH37t0lXi8qKiI1NfWoz7+Cm6Pk6+tLnz59mD17tnub0+lk9uzZDBo0qA5b1vBZlsWECRP49ttv+f3332nVqlWJ1/v06YOPj0+Jc79u3ToSExN17qvopJNOYsWKFSQkJLhvffv2Zdy4ce7HOtfVY8iQIaVKGqxfv56WLVsC0KpVK2JiYkqc64yMDObPn69zXUU5OTklFl4G8PLywul0AjrXNaky53bQoEGkpaWxePFi9z6///47TqeTAQMGHF0DjiodWSzLMlPB/fz8rPfff99avXq1de2111qNGjWykpOT67ppDdoNN9xghYWFWXPmzLGSkpLct5ycHPc+119/vdWiRQvr999/txYtWmQNGjTIGjRoUB222nMcPFvKsnSuq8uCBQssb29v64knnrA2bNhgffLJJ1ZgYKD18ccfu/d56qmnrEaNGlnff/+9tXz5cuvss8/W9OQjMH78eKtZs2buqeDffPONFRkZad11113ufXSuj1xmZqa1dOlSa+nSpRZgTZkyxVq6dKm1bds2y7Iqd25PPfVUq1evXtb8+fOtf/75x2rXrp2mgtcnr7zyitWiRQvL19fX6t+/v/Xff//VdZMaPKDM23vvvefeJzc317rxxhutxo0bW4GBgdY555xjJSUl1V2jPcihwY3OdfX54YcfrK5du1p+fn5Wx44drbfeeqvE606n03rggQes6Ohoy8/PzzrppJOsdevW1VFrG66MjAzr1ltvtVq0aGH5+/tbrVu3tu677z4rPz/fvY/O9ZH7448/yvwdPX78eMuyKndu9+3bZ40dO9YKDg62QkNDrSuuuMLKzMw86rbZLOugUo0iIiIiDZxybkRERMSjKLgRERERj6LgRkRERDyKghsRERHxKApuRERExKMouBERERGPouBGREREPIqCGxE55tlsNr777ru6boaIVBMFNyJSpy6//HJsNlup26mnnlrXTRORBsq7rhsgInLqqafy3nvvldjm5+dXR60RkYZOPTciUuf8/PyIiYkpcWvcuDFghozeeOMNTjvtNAICAmjdujVfffVVieNXrFjB8OHDCQgIICIigmuvvZasrKwS+0ydOpUuXbrg5+dHbGwsEyZMKPH63r17OeeccwgMDKRdu3ZMnz69Zr+0iNQYBTciUu898MADnHfeeSxbtoxx48Zx0UUXsWbNGgCys7MZOXIkjRs3ZuHChXz55Zf89ttvJYKXN954g5tuuolrr72WFStWMH36dNq2bVviMx555BEuvPBCli9fzumnn864ceNITU2t1e8pItXkqJfeFBE5CuPHj7e8vLysoKCgErcnnnjCsiyzOvz1119f4pgBAwZYN9xwg2VZlvXWW29ZjRs3trKystyvz5gxw7Lb7VZycrJlWZbVtGlT67777iu3DYB1//33u59nZWVZgPXzzz9X2/cUkdqjnBsRqXMnnngib7zxRolt4eHh7seDBg0q8dqgQYNISEgAYM2aNfTo0YOgoCD360OGDMHpdLJu3TpsNhu7du3ipJNOqrAN3bt3dz8OCgoiNDSU3bt3H+lXEpE6pOBGROpcUFBQqWGi6hIQEFCp/Xx8fEo8t9lsOJ3OmmiSiNQw5dyISL3333//lXreqVMnADp16sSyZcvIzs52vz537lzsdjsdOnQgJCSE+Ph4Zs+eXattFpG6o54bEalz+fn5JCcnl9jm7e1NZGQkAF9++SV9+/Zl6NChfPLJJyxYsIB3330XgHHjxvHQQw8xfvx4Hn74Yfbs2cPNN9/MpZdeSnR0NAAPP/ww119/PVFRUZx22mlkZmYyd+5cbr755tr9oiJSKxTciEidmzlzJrGxsSW2dejQgbVr1wJmJtNnn33GjTfeSGxsLNOmTaNz584ABAYG8ssvv3DrrbfSr18/AgMDOe+885gyZYr7vcaPH09eXh4vvPACd955J5GRkZx//vm19wVFpFbZLMuy6roRIiLlsdlsfPvtt4wePbqumyIiDYRybkRERMSjKLgRERERj6KcGxGp1zRyLiJVpZ4bERER8SgKbkRERMSjKLgRERERj6LgRkRERDyKghsRERHxKApuRERExKMouBERERGPouBGREREPIqCGxEREfEo/w9lmJm6FOpdRwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pbhpa7RhRnCK"
      },
      "source": [
        "**Cek Hasil Akurasi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vAZNdCKRpFy",
        "outputId": "2786c252-1c4b-489c-a131-31b6d8d49a7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Akurasi: 81.14%\n"
          ]
        }
      ],
      "source": [
        "print(\"Akurasi: {:.2%}\".format(test_acc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OvGv0nNSHfU"
      },
      "source": [
        "-----\n",
        "\n",
        "### **Jawab**\n",
        "\n",
        "**Buatlah model CNN untuk klasifikasi dataset MNIST.**<p>\n",
        "\n",
        "**Lakukan Import Library**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "iLRDLQr6SKhc"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h03zKCrYc50Q"
      },
      "source": [
        "**Memuat dataset MNIST**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhI0oCbUSPHI",
        "outputId": "ff15e138-dfe8-4011-9b30-45d63bee3ca9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYzOLwQLdBnV"
      },
      "source": [
        "**Normalisasi gambar ke rentang [0, 1]**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sOgQg0esSgSi"
      },
      "outputs": [],
      "source": [
        "\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84WokRi8dD_n"
      },
      "source": [
        "**Mengonversi label ke format one-hot encoding**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "AicO4sQoSgQ_"
      },
      "outputs": [],
      "source": [
        "\n",
        "train_labels = to_categorical(train_labels, num_classes=10)\n",
        "test_labels = to_categorical(test_labels, num_classes=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnWXT53OdHLT"
      },
      "source": [
        "**Menampilkan informasi tentang dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1QU3gXOV4Af",
        "outputId": "00dd2c8a-917d-4939-e5ce-c1e1b6c9e850"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah data latihan: 60000\n",
            "Jumlah data pengujian: 10000\n",
            "Ukuran gambar: (28, 28)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"Jumlah data latihan:\", len(train_images))\n",
        "print(\"Jumlah data pengujian:\", len(test_images))\n",
        "print(\"Ukuran gambar:\", train_images[0].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4Ht2KVcdKri"
      },
      "source": [
        "**Membangun model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "lZ-ByjBWWoRu"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLK1dBpVdORG"
      },
      "source": [
        "**Mengompilasi model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ntIbnldVWwzd"
      },
      "outputs": [],
      "source": [
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYkEh5LcdSUf"
      },
      "source": [
        "**Melatih model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQOJvNn5W2B1",
        "outputId": "2ffeeebd-717f-4798-b42e-8e7e973cb340"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "329/329 - 44s - loss: 0.3658 - accuracy: 0.8878 - val_loss: 0.0842 - val_accuracy: 0.9744 - 44s/epoch - 133ms/step\n",
            "Epoch 2/10\n",
            "329/329 - 37s - loss: 0.0995 - accuracy: 0.9703 - val_loss: 0.0601 - val_accuracy: 0.9811 - 37s/epoch - 112ms/step\n",
            "Epoch 3/10\n",
            "329/329 - 38s - loss: 0.0681 - accuracy: 0.9796 - val_loss: 0.0635 - val_accuracy: 0.9814 - 38s/epoch - 116ms/step\n",
            "Epoch 4/10\n",
            "329/329 - 36s - loss: 0.0548 - accuracy: 0.9839 - val_loss: 0.0434 - val_accuracy: 0.9867 - 36s/epoch - 110ms/step\n",
            "Epoch 5/10\n",
            "329/329 - 37s - loss: 0.0430 - accuracy: 0.9873 - val_loss: 0.0490 - val_accuracy: 0.9857 - 37s/epoch - 113ms/step\n",
            "Epoch 6/10\n",
            "329/329 - 35s - loss: 0.0365 - accuracy: 0.9891 - val_loss: 0.0435 - val_accuracy: 0.9871 - 35s/epoch - 107ms/step\n",
            "Epoch 7/10\n",
            "329/329 - 48s - loss: 0.0308 - accuracy: 0.9905 - val_loss: 0.0417 - val_accuracy: 0.9884 - 48s/epoch - 147ms/step\n",
            "Epoch 8/10\n",
            "329/329 - 38s - loss: 0.0272 - accuracy: 0.9917 - val_loss: 0.0410 - val_accuracy: 0.9888 - 38s/epoch - 117ms/step\n",
            "Epoch 9/10\n",
            "329/329 - 42s - loss: 0.0231 - accuracy: 0.9926 - val_loss: 0.0512 - val_accuracy: 0.9859 - 42s/epoch - 127ms/step\n",
            "Epoch 10/10\n",
            "329/329 - 35s - loss: 0.0185 - accuracy: 0.9940 - val_loss: 0.0489 - val_accuracy: 0.9880 - 35s/epoch - 108ms/step\n"
          ]
        }
      ],
      "source": [
        "\n",
        "history = model.fit(train_images, train_labels, epochs=10, batch_size=128, verbose=2, validation_split=0.3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL_8xxfodWFv"
      },
      "source": [
        "**Plot Visualisasi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "KS68NP1wXNLQ",
        "outputId": "f0b4925b-2cb9-4fe6-dd5a-d7a212504462"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(14, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Akurasi Pelatihan')\n",
        "plt.plot(history.history['val_accuracy'], label='Akurasi Validasi')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Akurasi Pelatihan dan Validasi')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Loss Pelatihan')\n",
        "plt.plot(history.history['val_loss'], label='Loss Validasi')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss Pelatihan dan Validasi')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTklRzwlddj6"
      },
      "source": [
        "**Evaluasi model pada kumpulan data uji**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQmGoqPMbSGP",
        "outputId": "3f4ac716-d0c7-4f05-e061-631d5dc0b5ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss Validasi: 0.035846225917339325\n",
            "Akurasi Validasi: 0.9904999732971191\n"
          ]
        }
      ],
      "source": [
        "\n",
        "validation_loss, validation_accuracy = model.evaluate(test_images, test_labels, verbose=False)\n",
        "print(\"Loss Validasi:\", validation_loss)\n",
        "print(\"Akurasi Validasi:\", validation_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlarRTEbdh4X"
      },
      "source": [
        "**Menghasilkan nilai prediksi pada kumpulan data uji**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3Df6-NcbVDq",
        "outputId": "42e2e0f6-407f-4ed0-8819-b2fbba8c50c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 3s 9ms/step\n"
          ]
        }
      ],
      "source": [
        "\n",
        "y_pred_enc = model.predict(test_images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kS_ZfywdmP4"
      },
      "source": [
        "**Mendekode nilai prediksi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "opBkwYnWbXix"
      },
      "outputs": [],
      "source": [
        "\n",
        "y_pred = [np.argmax(i) for i in y_pred_enc]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Vcl_4HZdo4w"
      },
      "source": [
        "**Menampilkan laporan klasifikasi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGovUP1JbcjV",
        "outputId": "a265cf2e-7ac0-429d-ef36-80fe4d0ee3ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       980\n",
            "           1       1.00      0.99      1.00      1135\n",
            "           2       0.99      1.00      0.99      1032\n",
            "           3       0.99      0.99      0.99      1010\n",
            "           4       0.99      1.00      0.99       982\n",
            "           5       0.98      0.99      0.98       892\n",
            "           6       0.99      0.98      0.99       958\n",
            "           7       0.99      0.99      0.99      1028\n",
            "           8       0.99      0.98      0.99       974\n",
            "           9       0.99      0.98      0.99      1009\n",
            "\n",
            "    accuracy                           0.99     10000\n",
            "   macro avg       0.99      0.99      0.99     10000\n",
            "weighted avg       0.99      0.99      0.99     10000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(classification_report(np.argmax(test_labels, axis=1), y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqtEdlOvdsBF"
      },
      "source": [
        "**Membuat matriks konfusi dan menampilkannya dengan heatmap**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "Bo30d_Eabevx",
        "outputId": "92a94593-7e29-4891-a750-4dced6fb79cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(58.222222222222214, 0.5, 'Aktual')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "fig, ax = plt.subplots(figsize=(7, 7))\n",
        "sns.heatmap(confusion_matrix(np.argmax(test_labels, axis=1), y_pred), annot=True, cbar=False, fmt='1d', cmap='Greens', ax=ax)\n",
        "ax.set_title('Matriks Konfusi', loc='left', fontsize=16)\n",
        "ax.set_xlabel('Prediksi')\n",
        "ax.set_ylabel('Aktual')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQK_pe5gduUc"
      },
      "source": [
        "**Menampilkan target yang diprediksi pada setiap gambar**\n",
        "(label di atas gambar adalah label yang diprediksi)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "E-MFvfoAbhoa",
        "outputId": "d392bc51-57cf-4f0a-b82e-41f77c80b43f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1400x800 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(14, 8))\n",
        "for ind, row in enumerate(test_images[:15]):\n",
        "    plt.subplot(3, 5, ind+1)\n",
        "    plt.title(y_pred[ind])\n",
        "    img = row.reshape(28, 28)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(img, cmap='cividis')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}