36-SN12_KNN-Image-Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "36-SN12_KNN-Image-Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prissyyy/Studi-Independen/blob/main/36-SN12_KNN-Image-Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZlxmRlaEyvx"
      },
      "source": [
        "Senin, 8 November 2021 (Image Classification in CV)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01_qRquKFLv9"
      },
      "source": [
        "REFERENSI\n",
        "\n",
        "- [Open CV Face Recognition Berbasis Algoritma Haar Cascade in medium.com](https://medium.com/purwadhikaconnect/opencv-face-recognition-berbasis-algoritma-haar-cascade-1a5e23d9e8cb)\n",
        "- [Jurnal ttg Implementasi Face Mask Detection Berbasis Machine Learning](http://journal.stmikjayakarta.ac.id/index.php/jisamar/article/view/399)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vM89u9B8uD7"
      },
      "source": [
        "#import library yang digunakan\n",
        "import pickle, gzip\n",
        "import numpy as np\n",
        "import urllib.request"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yTy6p0iR9jX"
      },
      "source": [
        "#Download dataset dari dropbox\n",
        "url = \"https://www.dropbox.com/s/d3hz2dli4z6imfl/mnist_1000.pkl.gz?dl=1\"\n",
        "u = urllib.request.urlopen(url)\n",
        "data = u.read()\n",
        "u.close()\n",
        " \n",
        "with open('mnist_1000.pkl.gz', \"wb\") as f :\n",
        "    f.write(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm7p-Q-583hE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b341f29-a57e-4568-8de0-0ffa1a1affbd"
      },
      "source": [
        "## load dataset\n",
        "\n",
        "f = gzip.open('/content/mnist_1000.pkl.gz', 'rb')\n",
        "trainData, trainLabels, valData, valLabels, testData, testLabels = pickle.load(\n",
        "    f, encoding='latin1')\n",
        "f.close()\n",
        " \n",
        "print(\"training data points: {}\".format(len(trainLabels)))\n",
        "print(\"validation data points: {}\".format(len(valLabels)))\n",
        "print(\"testing data points: {}\".format(len(testLabels)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training data points: 1000\n",
            "validation data points: 200\n",
            "testing data points: 200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaMPcVPmSEfJ",
        "outputId": "d862fe78-ba1f-473c-d610-0733184b7f37"
      },
      "source": [
        "#Lihat label data\n",
        "\n",
        "print('label datanya : ', np.unique(trainLabels))\n",
        "print('ukuran matriks tiap datanya : ', trainData[0].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label datanya :  [0 1 2 3 4 5 6 7 8 9]\n",
            "ukuran matriks tiap datanya :  (784,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "6v-A6BehUdD-",
        "outputId": "296e130f-a5d9-40e9-8591-9af8a346acd2"
      },
      "source": [
        "#Visualisasikan 10 data training pertama:\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for i in range(10):\n",
        "  image = trainData[i].reshape((32,32))\n",
        "  print('label = ', trainLabels[i])\n",
        "  plt.imshow(image, cmap=\"Greys\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-5bf0a650e5c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'label = '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainLabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Greys\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 784 into shape (32,32)"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEgmjvLrUiDK"
      },
      "source": [
        "Ups... kok code diatas gak bisa/error? karena ukuran reshapenya disitu 32x32, sementara data matriks aslinya disimpan dalam ukuran 784x1 (lihat output kodingan diatasnya). 32x32 tidak sama dengan 784x1, sehingga dia tidak mau direshape ke 32x32. Harus yang sama hasilnya, misalnya 28x28 (karena 28x28 = 784), seperti dibawah ini :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hk3KBmuESMwz",
        "outputId": "548e8299-922a-424e-d8de-80ac0412a2bd"
      },
      "source": [
        "#Visualisasikan 10 data training pertama:\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for i in range(10):\n",
        "  image = trainData[i].reshape((28,28))\n",
        "  print('label = ', trainLabels[i])\n",
        "  plt.imshow(image, cmap=\"Greys\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOhUlEQVR4nO3dfaxU9Z3H8c8XaDXyEFFuCBGyF4nGkMXSOsE1NZWVWEFNsDHBYqzUEGl8Sps0UdNNqH9oQtalSOKCwoqwSwshtkZ8yG5daCQQJQ6GRdT4sAYCCNyLRpAIlIfv/nEP7i3e+Z3LnDMP8n2/kpuZOd8593w58OHMnN/M+Zm7C8C5b0CrGwDQHIQdCIKwA0EQdiAIwg4EMaiZGxsxYoR3dnY2c5NAKDt27NCBAwesr1qhsJvZVEkLJQ2U9G/uPi/1/M7OTlWr1SKbBJBQqVRq1up+GW9mAyX9q6RpksZLmmlm4+v9fQAaq8h79kmSPnb3T9z9r5JWS5peTlsAylYk7JdI2tXr8e5s2d8wszlmVjWzand3d4HNASii4Wfj3X2Ju1fcvdLR0dHozQGooUjY90ga0+vx6GwZgDZUJOxvSbrMzMaa2Xcl/VTS2nLaAlC2uofe3P2EmT0g6b/UM/S2zN3fLa0zAKUqNM7u7q9KerWkXgA0EB+XBYIg7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IIhCs7gCx44dS9aPHz9es7Zx48bkunv27EnWZ82alawPGsQ/794K7Q0z2yHpS0knJZ1w90oZTQEoXxn/9f2jux8o4fcAaCDeswNBFA27S/qzmW0xszl9PcHM5phZ1cyq3d3dBTcHoF5Fw36tu/9A0jRJ95vZj858grsvcfeKu1c6OjoKbg5AvQqF3d33ZLddkl6QNKmMpgCUr+6wm9lgMxt6+r6kH0vaXlZjAMpV5Gz8SEkvmNnp3/MHd//PUrpC03zxxRfJ+vz585P19evXJ+ubN28+6576K28cfu7cuQ3b9rdR3WF3908kfa/EXgA0EENvQBCEHQiCsANBEHYgCMIOBMF3AM8BqY8hL1y4MLluXv3IkSPJursn62PHjq1Zu/jii5PrbtmyJVl/5plnkvV77723Zi3ipzk5sgNBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIyzt4GjR48m64899liyvnjx4pq1gwcP1tVTf02YMCFZf/3112vWTpw4kVx35MiRyfr+/fuT9dSfnXF2AOcswg4EQdiBIAg7EARhB4Ig7EAQhB0IgnH2NrBp06Zkfd68eU3q5JvGjx+frG/YsCFZHzZsWM3aZ599VldPqA9HdiAIwg4EQdiBIAg7EARhB4Ig7EAQhB0IgnH2NrB8+fKG/e7LL788Wb/++uuT9ccffzxZT42j59m5c2fd6+Ls5R7ZzWyZmXWZ2fZeyy4ys9fM7KPsdnhj2wRQVH9exi+XNPWMZY9IWuful0lalz0G0MZyw+7uGyR9fsbi6ZJWZPdXSLq15L4AlKzeE3Qj3X1vdn+fpJoXCzOzOWZWNbNqak4yAI1V+Gy898zsV3N2P3df4u4Vd69EvMgf0C7qDft+MxslSdltV3ktAWiEesO+VtKs7P4sSS+W0w6ARskdZzezVZImSxphZrsl/VbSPElrzGy2pJ2SZjSyyXPdokWLkvVrrrkmWZ869czBkv+Xd+31wYMHJ+uN1NXFC8Jmyg27u8+sUZpSci8AGoiPywJBEHYgCMIOBEHYgSAIOxAEX3FtA0OHDk3W77vvviZ10lzr169vdQuhcGQHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAYZw/u+eefT9YPHTqUrPdcqKg2M6tZ27JlS3LdPDfffHOyfumllxb6/ecajuxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EATj7N8Cx48fT9Y//fTTmrW5c+cm1125cmVdPZ126tSpZH3AgPqPJ2PGjEnWn3vuuYZt+1zE3gCCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIBhnb4KTJ08m67t3707WJ0+enKzv2rWrZu2CCy5Irps3lj1t2rRkfdWqVcn64cOHk/WUEydOJOuvvPJKsn7HHXfUrA0cOLCunr7Nco/sZrbMzLrMbHuvZY+a2R4z25r93NTYNgEU1Z+X8cslTe1j+QJ3n5j9vFpuWwDKlht2d98g6fMm9AKggYqcoHvAzLZlL/OH13qSmc0xs6qZVbu7uwtsDkAR9YZ9saRxkiZK2itpfq0nuvsSd6+4e6Wjo6POzQEoqq6wu/t+dz/p7qckLZU0qdy2AJStrrCb2aheD38iaXut5wJoD7nj7Ga2StJkSSPMbLek30qabGYTJbmkHZJ+0cAe217eOPrWrVuT9auvvrrQ9hctWlSzNmXKlOS648aNS9aPHDmSrG/bti1Z37x5c7Kesm/fvmT97rvvTtZT143P2+eDBp17H0HJ/RO5+8w+Fj/bgF4ANBAflwWCIOxAEIQdCIKwA0EQdiCIc298oUFSw2sLFy5MrvvQQw8V2nbqq5qSdNddd9WsnX/++cl1v/rqq2T9lltuSdbffPPNZP28886rWXviiSeS6+YNWeZdSvq6666rWZsxY0Zy3bxLcA8ZMiRZzzN69OhC69eDIzsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBME4eyZv6uEnn3yyZu3hhx9Orjt06NBkffny5cn6jTfemKynxtJ37tyZXPeee+5J1jds2JCsT5gwIVlfvXp1zdoVV1yRXPfYsWPJ+oMPPpisL1u2rGZtxYoVyXXXrFmTrOdJfb1Wkj788MNCv78eHNmBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjG2TMvv/xysp4aS8/7bvNLL72UrF911VXJ+gcffJCsP/300zVrK1euTK6bd6nop556KlnP+679sGHDkvWU1HfhJenKK69M1lOfjbjtttuS6y5dujRZz7NgwYJC6zcCR3YgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCMLcvWkbq1QqXq1Wm7a9s5F3He/U9MF512bPG0c/ePBgsr59+/ZkvYjFixcn67Nnz07WBwzgeNFOKpWKqtWq9VXL/ZsyszFm9hcze8/M3jWzX2bLLzKz18zso+x2eNmNAyhPf/5bPiHp1+4+XtI/SLrfzMZLekTSOne/TNK67DGANpUbdnff6+5vZ/e/lPS+pEskTZd0+to+KyTd2qgmARR3Vm+4zKxT0vclbZY00t33ZqV9kkbWWGeOmVXNrNrd3V2gVQBF9DvsZjZE0h8l/crdD/Wuec9Zvj7P9Ln7EnevuHulo6OjULMA6tevsJvZd9QT9N+7+5+yxfvNbFRWHyWpqzEtAihD7ldczcwkPSvpfXf/Xa/SWkmzJM3Lbl9sSIdN0tnZmaynht6OHj2aXHfTpk31tPS1O++8M1m/4YYbatamTZuWXPfCCy9M1hlaO3f05/vsP5T0M0nvmNnpCbN/o56QrzGz2ZJ2SkpPeA2gpXLD7u4bJfU5SC9pSrntAGgUXqMBQRB2IAjCDgRB2IEgCDsQBJeSzqxbty5Zf+ONN2rW8sbRR40alazffvvtyXreV2gHDhyYrAMSR3YgDMIOBEHYgSAIOxAEYQeCIOxAEIQdCIJx9kze9MCTJ0+uqwa0C47sQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EERu2M1sjJn9xczeM7N3zeyX2fJHzWyPmW3Nfm5qfLsA6tWfi1eckPRrd3/bzIZK2mJmr2W1Be7+L41rD0BZ+jM/+15Je7P7X5rZ+5IuaXRjAMp1Vu/ZzaxT0vclbc4WPWBm28xsmZkNr7HOHDOrmlm1u7u7ULMA6tfvsJvZEEl/lPQrdz8kabGkcZImqufIP7+v9dx9ibtX3L3S0dFRQssA6tGvsJvZd9QT9N+7+58kyd33u/tJdz8laamkSY1rE0BR/Tkbb5KelfS+u/+u1/LeU5P+RNL28tsDUJb+nI3/oaSfSXrHzLZmy34jaaaZTZTkknZI+kVDOgRQiv6cjd8oyfoovVp+OwAahU/QAUEQdiAIwg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgjB3b97GzLol7ey1aISkA01r4Oy0a2/t2pdEb/Uqs7e/c/c+r//W1LB/Y+NmVXevtKyBhHbtrV37kuitXs3qjZfxQBCEHQii1WFf0uLtp7Rrb+3al0Rv9WpKby19zw6geVp9ZAfQJIQdCKIlYTezqWb2gZl9bGaPtKKHWsxsh5m9k01DXW1xL8vMrMvMtvdadpGZvWZmH2W3fc6x16Le2mIa78Q04y3dd62e/rzp79nNbKCkDyXdIGm3pLckzXT395raSA1mtkNSxd1b/gEMM/uRpMOS/t3d/z5b9s+SPnf3edl/lMPd/eE26e1RSYdbPY13NlvRqN7TjEu6VdLP1cJ9l+hrhpqw31pxZJ8k6WN3/8Td/ypptaTpLeij7bn7Bkmfn7F4uqQV2f0V6vnH0nQ1emsL7r7X3d/O7n8p6fQ04y3dd4m+mqIVYb9E0q5ej3erveZ7d0l/NrMtZjan1c30YaS7783u75M0spXN9CF3Gu9mOmOa8bbZd/VMf14UJ+i+6Vp3/4GkaZLuz16utiXveQ/WTmOn/ZrGu1n6mGb8a63cd/VOf15UK8K+R9KYXo9HZ8vagrvvyW67JL2g9puKev/pGXSz264W9/O1dprGu69pxtUG+66V05+3IuxvSbrMzMaa2Xcl/VTS2hb08Q1mNjg7cSIzGyzpx2q/qajXSpqV3Z8l6cUW9vI32mUa71rTjKvF+67l05+7e9N/JN2knjPy/yvpn1rRQ42+LpX0P9nPu63uTdIq9bysO66ecxuzJV0saZ2kjyT9t6SL2qi3/5D0jqRt6gnWqBb1dq16XqJvk7Q1+7mp1fsu0VdT9hsflwWC4AQdEARhB4Ig7EAQhB0IgrADQRB2IAjCDgTxfxCyZWiOyYzMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label =  4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMz1VoLq9DBO"
      },
      "source": [
        "#import KNN Classifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mq8AUd4W9HE1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a97b3fc8-ff54-4a20-a3c2-425226742ede"
      },
      "source": [
        "## Melatih model\n",
        " \n",
        "for k in [1, 3, 5, 7, 9, 11, 13, 15, 25]:\n",
        "    model = KNeighborsClassifier(n_neighbors=k)\n",
        "    model.fit(trainData, trainLabels)\n",
        " \n",
        "    score = model.score(valData, valLabels)\n",
        "    print(k, score) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.88\n",
            "3 0.88\n",
            "5 0.86\n",
            "7 0.845\n",
            "9 0.85\n",
            "11 0.835\n",
            "13 0.815\n",
            "15 0.81\n",
            "25 0.795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUfWJP719KQE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "154c4424-a1a3-49fa-ad40-90ebc9e8cab9"
      },
      "source": [
        "# melatih ulang classifier menggunakan nilai k terbaik value dan memprediksikan label\n",
        "model = KNeighborsClassifier(n_neighbors=3)\n",
        "model.fit(trainData, trainLabels)\n",
        "accuracy = model.score(testData, testLabels)\n",
        "print(\"Achieved accuracy of %.2f%% on test data\" % (accuracy*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Achieved accuracy of 85.50% on test data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUT2rBwY9MRh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3d2ec50-6da8-495b-aeb4-dc994afee419"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "predictions = model.predict(testData)\n",
        "print(\"Classification Report\")\n",
        "print(classification_report(testLabels, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      1.00      0.92        17\n",
            "           1       0.80      1.00      0.89        28\n",
            "           2       0.80      0.75      0.77        16\n",
            "           3       0.92      0.75      0.83        16\n",
            "           4       0.95      0.68      0.79        28\n",
            "           5       0.94      0.85      0.89        20\n",
            "           6       1.00      0.90      0.95        20\n",
            "           7       0.88      0.92      0.90        24\n",
            "           8       1.00      0.70      0.82        10\n",
            "           9       0.66      0.90      0.76        21\n",
            "\n",
            "    accuracy                           0.85       200\n",
            "   macro avg       0.88      0.84      0.85       200\n",
            "weighted avg       0.87      0.85      0.85       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ug62-h8d9OK3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbf44d50-2a58-46f0-9a6b-fe7539ab56a3"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print(\"Confusion Matrix\")\n",
        "print(confusion_matrix(testLabels, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            "[[17  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 28  0  0  0  0  0  0  0  0]\n",
            " [ 1  2 12  0  0  0  0  1  0  0]\n",
            " [ 0  2  1 12  0  0  0  0  0  1]\n",
            " [ 0  1  0  0 19  0  0  0  0  8]\n",
            " [ 0  1  0  1  1 17  0  0  0  0]\n",
            " [ 2  0  0  0  0  0 18  0  0  0]\n",
            " [ 0  1  0  0  0  0  0 22  0  1]\n",
            " [ 0  0  2  0  0  1  0  0  7  0]\n",
            " [ 0  0  0  0  0  0  0  2  0 19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3fl4mwkeiqL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ceff71fe-c388-48a7-b82b-2eff59571eb2"
      },
      "source": [
        "testData"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}