classifierProject.ipynb

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     "text": [
      "   suka  masak  nasi  goreng  ukur  makan  beras  tanam  sawah  mobil  bus  \\\n",
      "0     2      3     3       3     0      0      0      0      0      0    0   \n",
      "1     0      0     2       0     1      1      0      0      0      0    0   \n",
      "2     0      0     1       0     0      0      2      2      2      0    0   \n",
      "3     0      0     0       0     1      0      0      0      0      4    2   \n",
      "4     0      0     0       0     1      0      0      0      1      0    2   \n",
      "5     1      2     4       2     1      1      1      0      0      0    0   \n",
      "6     1      3     2       1     1      2      0      0      0      0    0   \n",
      "7     3      0     0       0     2      0      0      0      0      0    1   \n",
      "8     1      0     3       0     2      3      1      2      3      0    0   \n",
      "\n",
      "   angkut  tumpang     LABEL  \n",
      "0       0        0   kuliner  \n",
      "1       0        0   kuliner  \n",
      "2       0        0   ekonomi  \n",
      "3       1        1  otomotif  \n",
      "4       0        0  otomotif  \n",
      "5       0        0   kuliner  \n",
      "6       0        0   kuliner  \n",
      "7       1        3  otomotif  \n",
      "8       0        1   ekonomi  \n",
      "{'suka': 0, 'masak': 0, 'nasi': 0, 'goreng': 0, 'ukur': 0, 'makan': 0, 'beras': 3, 'tanam': 2, 'sawah': 1, 'mobil': 0, 'bus': 1, 'angkut': 0, 'tumpang': 0}\n",
      "{'beras': 3, 'tanam': 2, 'sawah': 1, 'bus': 1}\n",
      "['beras', 'tanam', 'sawah', 'bus']\n",
      "\n",
      "['kuliner', 'ekonomi', 'otomotif']\n",
      "Peluang masuk kategori kuliner adalah : 7.3221185468355295e-09\n",
      "Peluang masuk kategori ekonomi adalah : 4.393271128101318e-06\n",
      "Peluang masuk kategori otomotif adalah : 8.237383365189971e-09\n",
      "\n",
      "Jadi, document ini termasuk pada kategori : ekonomi\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import math\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "import re\n",
    "import string\n",
    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
    "\n",
    "class preProcessing():\n",
    "    def parsing(dokumen): if \n",
    "        stopword = open(\"stopword.txt\",\"r\").read().split(\"\\n\")\n",
    "        dokumen = dokumen.lower().split()\n",
    "        dokumen = [kata for kata in dokumen if kata not in stopword]\n",
    "        # print(dokumen)\n",
    "        return dokumen\n",
    "    \n",
    "    def cleaning(dokumen):\n",
    "        tandabaca = string.punctuation\n",
    "        domain = re.compile(r'[\\S]+\\.(net|com|org)[\\S]*\\s?')\n",
    "        dokumenproses = dokumen.strip()\n",
    "        dokumenproses = domain.sub('',dokumenproses)\n",
    "        hasil = ''\n",
    "        for i,karakter in enumerate(dokumenproses):\n",
    "            if (karakter in tandabaca or karakter.isnumeric()):\n",
    "                if (i != len(dokumenproses)-1):\n",
    "                    nextkarakter = dokumenproses[i+1]\n",
    "                    if (nextkarakter not in tandabaca and not nextkarakter.isnumeric() and nextkarakter != ' '):\n",
    "                        hasil += ' '\n",
    "            else:\n",
    "                hasil += karakter\n",
    "    #     print(hasil)\n",
    "        return hasil\n",
    "\n",
    "    # def type(dokumen):\n",
    "    #     return list(OrderedDict(kata,None)for kata in dokumen).keys()\n",
    "\n",
    "    def stemming(dokumen):\n",
    "    #     print([createstr.stem(kata) for kata in dokumen if kata and kata.isascii()])\n",
    "        return [StemmerFactory().create_stemmer().stem(kata) for kata in dokumen if kata and kata.isascii()]\n",
    "\n",
    "    def preprocessing(dokumen):\n",
    "        return stemming(\n",
    "            parsing(\n",
    "                cleaning(dokumen)\n",
    "            )\n",
    "        )\n",
    "\n",
    "    def main():\n",
    "        direktori = input(\"masukkan direktori yang ingin dibaca: \")\n",
    "        if(direktori.lower().endswith(\".txt\")):\n",
    "            file = open(direktori,\"r\",encoding=\"ISO-8859-1\")\n",
    "            konten = file.read()\n",
    "            file.close()\n",
    "            hasil = preprocessing(konten)\n",
    "            print(hasil)\n",
    "\n",
    "class naifeBayes():\n",
    "    def __init__(self,data,kolom):\n",
    "        self.data=data\n",
    "        self.kolom=kolom\n",
    "        self.label = []\n",
    "        for i in range(len(self.data)):\n",
    "            if(self.data['LABEL'][i]) not in self.label:\n",
    "                self.label+=[self.data['LABEL'][i]]\n",
    "\n",
    "    def prints(self):\n",
    "        print(self.data,self.kolom,self.label)\n",
    "\n",
    "    def prediksi(self,pred1,predk):\n",
    "        high = 0\n",
    "        for i in self.label:\n",
    "            hasil = self.pcd(i,pred1,predk)\n",
    "            if hasil>high:\n",
    "                high = hasil\n",
    "                kats = i\n",
    "        return kats\n",
    "\n",
    "    def pcd(self,c,pred1,predk):\n",
    "        temp = 1\n",
    "        for i in predk:\n",
    "            temps = 1\n",
    "            if(pred1[i]>1):\n",
    "                temps = math.pow(self.pdc(i,c),pred1[i])\n",
    "                temp = temp*temps\n",
    "            else:\n",
    "                temp = temp*self.pdc(i,c)\n",
    "        temp = temp*self.pc(c)\n",
    "        return temp\n",
    "\n",
    "    def pc(self,c):\n",
    "        c = str(c)\n",
    "        count = 0\n",
    "        leng = len(data)\n",
    "        for i in range(leng):\n",
    "            if (data['LABEL'][i]==c):\n",
    "                count+=1\n",
    "        temp = count/leng\n",
    "        return temp\n",
    "\n",
    "    def pdc(self,w,c):\n",
    "        countv = len(kolom)\n",
    "        sumw = 0\n",
    "        countwc = 0\n",
    "        for i in range(len(data)):\n",
    "            sumw+=data[w][i]\n",
    "            if(data['LABEL'][i]==c):\n",
    "                countwc+=data[w][i]\n",
    "        atas = countwc + 1\n",
    "        bawah = sumw + countv\n",
    "        temp = atas/bawah\n",
    "        return temp\n",
    "    \n",
    "\n",
    "data = pd.read_csv(\"Testes.csv\")\n",
    "# ambil kolom sebagai list dengan nama kolom\n",
    "kolom=[]\n",
    "with open(\"Testes.csv\") as csf:\n",
    "    readCSV = csv.reader(csf,delimiter=\",\")\n",
    "    for i in readCSV:\n",
    "        kolom += i\n",
    "        kolom = kolom[:-1]\n",
    "        break\n",
    "\n",
    "# untuk diprediksi nanti\n",
    "# memetakan kata unik / kolom yang ada\n",
    "l1st=[0,0,0,0,0,0,3,2,1,0,1,0,0]\n",
    "pred = dict()\n",
    "pred1 = dict()\n",
    "predk=[]\n",
    "j=0\n",
    "for i in kolom:\n",
    "    pred[i] = l1st[j]\n",
    "    if pred[i]>0:\n",
    "        pred1[i] = l1st[j]\n",
    "        predk+=[i]\n",
    "    j+=1\n",
    "    \n",
    "print(data)\n",
    "print(pred)\n",
    "print(pred1)\n",
    "print(predk)\n",
    "print()\n",
    "c = naifeBayes(data,kolom)\n",
    "print(c.label)\n",
    "high= 0\n",
    "kats =''\n",
    "\n",
    "for i in c.label:\n",
    "    hasil = c.pcd(i,pred1,predk)\n",
    "    print(\"Peluang masuk kategori\",i,\"adalah :\",hasil)\n",
    "\n",
    "print()\n",
    "print(\"Jadi, document ini termasuk pada kategori :\",c.prediksi(pred1,predk))"
   ]
  }
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