fuzzyLogic.ipynb

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  "metadata": {
    "colab": {
      "name": "fuzzyLogic.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python392jvsc74a57bd0814d12259f8231896cbd61fdcfa13fb9f60bca0ace20e4cb9cb282a2368bc8fd",
      "display_name": "Python 3.9.2 64-bit"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.2"
    },
    "metadata": {
      "interpreter": {
        "hash": "814d12259f8231896cbd61fdcfa13fb9f60bca0ace20e4cb9cb282a2368bc8fd"
      }
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QRNejqcXws9"
      },
      "source": [
        "# **LIBRARY**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKeRrsJFwM0u"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wI8cc6lX_1k"
      },
      "source": [
        "# **READ DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "V9CyAhZkwX90",
        "outputId": "6c054611-8584-489e-b06d-c75130083aed"
      },
      "source": [
        "data = pd.read_excel(\"https://github.com/serlysetyani/fuzzyLogic/blob/main/restoran.xlsx?raw=true\")\n",
        "data"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id  pelayanan  makanan\n",
              "0     1         58        7\n",
              "1     2         54        1\n",
              "2     3         98        2\n",
              "3     4         52        4\n",
              "4     5         11        4\n",
              "..  ...        ...      ...\n",
              "95   96         30        1\n",
              "96   97         25        3\n",
              "97   98         27       10\n",
              "98   99          8        6\n",
              "99  100         11        8\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>pelayanan</th>\n      <th>makanan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>58</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>54</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>98</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>52</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>11</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>96</td>\n      <td>30</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>97</td>\n      <td>25</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>98</td>\n      <td>27</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>99</td>\n      <td>8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 3 columns</p>\n</div>"
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmUN4cS1KnLE"
      },
      "source": [
        "# **MEMBERSHIP**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puffLA0dLAGz"
      },
      "source": [
        "\n",
        "\n",
        "*   MEMBERSHIP MAKANAN\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6T2PuqIy59A"
      },
      "source": [
        "def poorFood(f):\n",
        "    poorFood, notpoorFood = 4, 6\n",
        "\n",
        "    if f <= poorFood:\n",
        "        return 1\n",
        "    elif f > notpoorFood:\n",
        "        return 0\n",
        "    elif f > poorFood and f <= notpoorFood:\n",
        "        return ((notpoorFood - f) / (notpoorFood - poorFood))\n",
        "\n",
        "\n",
        "def goodFood(f):\n",
        "    notgoodFood1, goodFood1, goodFood2, notgoodFood2 = 4, 6, 7, 9\n",
        "\n",
        "    if f > goodFood1 and f <= goodFood2:\n",
        "        return 1\n",
        "    elif f <= notgoodFood1 or f > notgoodFood2:\n",
        "        return 0\n",
        "    elif f > notgoodFood1 and f <= goodFood1:\n",
        "        return ((f - notgoodFood1) / (goodFood1 - notgoodFood1))\n",
        "    elif f > goodFood2 and f <= notgoodFood2:\n",
        "        return ((notgoodFood2 - f) / (notgoodFood2 - goodFood2))\n",
        "\n",
        "\n",
        "def excellentFood(f):\n",
        "    notexcellentFood, excellentFood = 6, 8\n",
        "\n",
        "    if f > excellentFood:\n",
        "        return 1\n",
        "    elif f <= notexcellentFood:\n",
        "        return 0\n",
        "    elif f > notexcellentFood and f <= excellentFood:\n",
        "        return ((f - notexcellentFood) / (excellentFood - notexcellentFood))\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "acf2224e-530b-4de9-839c-31c70a377c7b"
      },
      "source": [
        "# plot grafik membership makanan\n",
        "\n",
        "x = [i for i in range(11)]\n",
        "\n",
        "ypoorF = [poorFood(i) for i in x]\n",
        "ygoodF = [goodFood(i) for i in x]\n",
        "yexcellentF = [excellentFood(i) for i in x]\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.title('Membership Makanan',fontsize = 20) \n",
        "plt.plot(x, ypoorF, label = 'Poor Food')\n",
        "plt.plot(x, ygoodF, label = 'Good Food')\n",
        "plt.plot(x, yexcellentF, label = 'Excellent Food')\n",
        "plt.xlabel('Nilai')\n",
        "plt.ylabel(r'$\\mu\\ (x)$')\n",
        "plt.legend()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x112147d8520>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ayh7ZGVLNGJ"
      },
      "source": [
        "*   MEMBERSHIP PELAYANAN\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkdMYLVFAiAu"
      },
      "source": [
        "def PoorService(x):\n",
        "  poorService = 50\n",
        "  notpoorService = 67\n",
        "\n",
        "  if x <= poorService:\n",
        "    return 1;\n",
        "  elif x > notpoorService:\n",
        "    return 0\n",
        "  elif x > poorService and x <= notpoorService:\n",
        "    return (notpoorService - x) / (notpoorService - poorService)\n",
        "\n",
        "def GoodService(x):\n",
        "  notgoodService1 = 50\n",
        "  goodService1 = 60\n",
        "  goodService2 = 70\n",
        "  notgoodService2 = 85\n",
        "\n",
        "  if x > goodService1 and x <= goodService2:\n",
        "    return 1\n",
        "  elif x <= notgoodService1 or x > notgoodService2:\n",
        "    return 0\n",
        "  elif x > notgoodService1 and x <= goodService1:\n",
        "    return (x - notgoodService1) / (goodService1 - notgoodService1)\n",
        "  elif x > goodService2 and x <= notgoodService2:\n",
        "    return (notgoodService2 - x) / (notgoodService2 - goodService2)\n",
        "\n",
        "def ExcellentService(x):\n",
        "  notexcellentService = 75\n",
        "  excellentService = 80\n",
        "\n",
        "  if x > excellentService:\n",
        "    return 1\n",
        "  elif x <= notexcellentService:\n",
        "    return 0\n",
        "  elif x > notexcellentService and x <= excellentService:\n",
        "    return (x - notexcellentService) / (excellentService - notexcellentService)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QI0ry3URfIMA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "cad93563-a7fe-453c-b225-5a2f6c8514af"
      },
      "source": [
        "# plot grafik membership pelayanan\n",
        "\n",
        "x = [i for i in range(101)]\n",
        "\n",
        "ypoorS = [PoorService(i) for i in x]\n",
        "ygoodS = [GoodService(i) for i in x]\n",
        "yexcellentS = [ExcellentService(i) for i in x]\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.title('Membership Pelayanan',fontsize = 20) \n",
        "plt.plot(x, ypoorS, label = 'Poor Service')\n",
        "plt.plot(x, ygoodS, label = 'Good Service')\n",
        "plt.plot(x, yexcellentS, label = 'Excellent Service')\n",
        "plt.xlabel('Nilai')\n",
        "plt.ylabel(r'$\\mu\\ (x)$')\n",
        "plt.legend()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x11216923040>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x288 with 1 Axes>",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvNTAMVzKRzY"
      },
      "source": [
        "# **FUZZIFICATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95ro0uapjS9j"
      },
      "source": [
        "def fuzzificationFood(foodValue):\n",
        "  foodSet = []\n",
        "  foodSet.append(poorFood(foodValue))\n",
        "  foodSet.append(goodFood(foodValue))\n",
        "  foodSet.append(excellentFood(foodValue))\n",
        "  return foodSet\n",
        "\n",
        "def fuzzificationService(serviceValue):\n",
        "  serviceSet = []\n",
        "  serviceSet.append(PoorService(serviceValue))\n",
        "  serviceSet.append(GoodService(serviceValue))\n",
        "  serviceSet.append(ExcellentService(serviceValue))\n",
        "  return serviceSet\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvHbFWsdKFkN"
      },
      "source": [
        "# **INFERENCE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeiBlAo2RYsR"
      },
      "source": [
        "def inference(serviceSet, foodSet):\n",
        "  inferenceSet = []\n",
        "  recommendedSet, moderatelySet, notRecommendedSet = [], [], []\n",
        "\n",
        "  recommendedSet.append(min(foodSet[2], serviceSet[2]))\n",
        "  recommendedSet.append(min(foodSet[2], serviceSet[1]))\n",
        "  recommendedSet.append(min(foodSet[1], serviceSet[2]))\n",
        "  \n",
        "\n",
        "  moderatelySet.append(min(foodSet[1], serviceSet[1]))\n",
        "  moderatelySet.append(min(foodSet[2], serviceSet[0]))\n",
        "\n",
        "  notRecommendedSet.append(min(foodSet[0], serviceSet[0]))\n",
        "  notRecommendedSet.append(min(foodSet[1], serviceSet[0]))\n",
        "  notRecommendedSet.append(min(foodSet[0], serviceSet[1]))\n",
        "  notRecommendedSet.append(min(foodSet[0], serviceSet[2]))\n",
        "\n",
        "  inferenceSet.append(max(recommendedSet))\n",
        "  inferenceSet.append(max(moderatelySet))\n",
        "  inferenceSet.append(max(notRecommendedSet))\n",
        "  \n",
        "  return inferenceSet"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rygp6k4cJ1vV"
      },
      "source": [
        "# **DEFUZZIFICATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vT8e3KxLfCZT"
      },
      "source": [
        "def defuzzification(inferenceSet):\n",
        "  multiplier = (inferenceSet[0]*100) + (inferenceSet[1]*80) + (inferenceSet[2]*50) \n",
        "  divider = inferenceSet[0] + inferenceSet[1] + inferenceSet[2]\n",
        "  return multiplier/divider"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "B71JA4GAb0PY",
        "outputId": "ce896ab5-c9ae-41d7-d214-7640f0bdad9d"
      },
      "source": [
        "xnotRecommended = [50,50]\n",
        "xmoderately = [80,80]\n",
        "xRecommended = [100,100]\n",
        "\n",
        "y = [0,1]\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.title('Defuzzifikasi',fontsize = 20)\n",
        "plt.plot(xnotRecommended,y,label='not Recommended')\n",
        "plt.plot(xmoderately,y,label='Moderately')\n",
        "plt.plot(xRecommended,y,label='Recommended')\n",
        "plt.xlabel('Nilai')\n",
        "plt.ylabel(r'$\\mu\\ output$')\n",
        "plt.legend()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x112169b3220>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x288 with 1 Axes>",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YclYlMSd8Ek"
      },
      "source": [
        "# **MAIN PROGRAM**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Z779wb_dGzD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "615a3067-64ab-4970-8f4e-552c80e0e594"
      },
      "source": [
        "hasilAkhir = []\n",
        "for row in range(100):\n",
        "  fuzziPelayanan = fuzzificationService(data['pelayanan'][row])\n",
        "  fuzziMakanan = fuzzificationFood(data['makanan'][row])\n",
        "  inferensi = inference(fuzziPelayanan,fuzziMakanan)\n",
        "  hasilAkhir.extend([defuzzification(inferensi)])\n",
        " \n",
        "data['hasil'] = hasilAkhir\n",
        "data = data.sort_values(by='hasil', ascending=False)[:10]\n",
        "data['id'].to_excel('peringkat.xls', index=False, header=False)\n",
        "\n",
        "data"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-c5dafaefbd58>:10: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n  data['id'].to_excel('peringkat.xls', index=False, header=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  pelayanan  makanan       hasil\n",
              "41  42         94       10  100.000000\n",
              "23  24        100        9  100.000000\n",
              "30  31         74        9  100.000000\n",
              "21  22         79        9  100.000000\n",
              "78  79         87        9  100.000000\n",
              "68  69         86       10  100.000000\n",
              "53  54         64       10   97.000000\n",
              "15  16         82        6   96.666667\n",
              "24  25         61       10   94.782609\n",
              "82  83         69        8   93.333333"
            ],
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>pelayanan</th>\n      <th>makanan</th>\n      <th>hasil</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>41</th>\n      <td>42</td>\n      <td>94</td>\n      <td>10</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>24</td>\n      <td>100</td>\n      <td>9</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>31</td>\n      <td>74</td>\n      <td>9</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>22</td>\n      <td>79</td>\n      <td>9</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>79</td>\n      <td>87</td>\n      <td>9</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>69</td>\n      <td>86</td>\n      <td>10</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>54</td>\n      <td>64</td>\n      <td>10</td>\n      <td>97.000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>82</td>\n      <td>6</td>\n      <td>96.666667</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>25</td>\n      <td>61</td>\n      <td>10</td>\n      <td>94.782609</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>83</td>\n      <td>69</td>\n      <td>8</td>\n      <td>93.333333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}