TF-IDF adalah singkatan dari “Term Frequency-Inverse Document Frequency“. Ini adalah metode feature extraction atau ekstraksi fitur yang digunakan untuk memberikan bobot pada kata-kata dalam sebuah dokumen atau korpus berdasarkan seberapa sering kata tersebut muncul dalam dokumen tersebut dan seberapa umum kata tersebut di seluruh korpus. Tujuan utama dari TF-IDF adalah untuk menemukan kata-kata yang paling penting dalam dokumen tertentu. Term Frequency-Inverse Document Frequency (TF-IDF) adalah metode yang digunakan untuk menilai kepentingan kata dalam suatu dokumen atau korpus berdasarkan seberapa sering kata tersebut muncul dalam dokumen tersebut dan seberapa umum kata tersebut di seluruh korpus.
TF-IDF memperhitungkan dua faktor penting dalam penentuan kepentingan kata dalam sebuah dokumen:
- Term Frequency (TF) – menghitung seberapa sering kata tertentu muncul dalam sebuah dokumen.
- Inverse Document Frequency (IDF) – menghitung seberapa umum kata tertentu di seluruh korpus dokumen.
Dengan menggabungkan kedua faktor tersebut, TF-IDF memberikan bobot pada kata dalam dokumen tersebut. Kata yang muncul lebih sering dalam dokumen dan kurang umum dalam seluruh korpus mendapatkan bobot yang lebih tinggi, sedangkan kata yang muncul lebih jarang dalam dokumen atau lebih umum dalam seluruh korpus mendapatkan bobot yang lebih rendah. Dengan cara ini, TF-IDF dapat membantu dalam mengidentifikasi kata-kata kunci atau topik yang paling penting dalam suatu dokumen atau korpus. TF-IDF sering digunakan dalam pengolahan bahasa alami, pemrosesan teks, dan analisis teks untuk mengklasifikasikan dokumen, melakukan clustering, dan mengekstrak informasi penting.
TF-IDF digunakan di bidang information retrieval (IR) pada Natural Language Processing (NLP) dan machine learning, yang dapat mengukur tingkat kepentingan atau relevansi representasi string (kata, frasa, lema, dan sebagainya) di dalam sebuah dokumen di antara sekumpulan dokumen (yang disebut juga sebagai corpus).
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import pandas as pd import re
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web_hoax = pd.read_excel(r"C:\Users\GK3v\Documents\Python\NaiveBayesTfidf\Preprocessing\databeritabaru.xlsx", header=0)
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print(web_hoax.shape)
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(667, 2)
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web_hoax.head()
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| Label | Article | |
|---|---|---|
| 0 | ham | Makanan rendah kalori cocok dikonsumsi untuk k… |
| 1 | ham | Sayuran merupakan makanan terbaik untuk keseha… |
| 2 | ham | Pada penderita diabetes, cek gula darah dilaku… |
| 3 | ham | Pisang merupakan salah satu jenis buah yang me… |
| 4 | ham | Kepala Badan Kependudukan dan Keluarga Berenca… |
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Casefolding
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import string
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def preprocessing_text(kalimat):
# Mengubah kalimat menjadi huruf kecil
lower_case = kalimat.lower()
#Menghapus angka dari kalimat
hasil = re.sub(r"\d+", "", lower_case)
#Menghapus tanda baca dari kalimat
hasil = hasil.translate(str.maketrans("","",string.punctuation))
#Menghapus spasi pada awal dan akhir kalimat
hasil = hasil.strip()
return hasil
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df = web_hoax df['text_clean'] = df['Article'].apply(preprocessing_text) df.head()
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| Label | Article | text_clean | |
|---|---|---|---|
| 0 | ham | Makanan rendah kalori cocok dikonsumsi untuk k… | makanan rendah kalori cocok dikonsumsi untuk k… |
| 1 | ham | Sayuran merupakan makanan terbaik untuk keseha… | sayuran merupakan makanan terbaik untuk keseha… |
| 2 | ham | Pada penderita diabetes, cek gula darah dilaku… | pada penderita diabetes cek gula darah dilakuk… |
| 3 | ham | Pisang merupakan salah satu jenis buah yang me… | pisang merupakan salah satu jenis buah yang me… |
| 4 | ham | Kepala Badan Kependudukan dan Keluarga Berenca… | kepala badan kependudukan dan keluarga berenca… |
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Tokenizing
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import nltk
nltk.download('all')
from nltk.tokenize import word_tokenize
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.
def tokenize_text(kalimat): tokens = nltk.tokenize.word_tokenize(kalimat) return tokens
.
df['token'] = df['text_clean'].apply(tokenize_text) df.head()
.
.
Filtering (Stopword Removal)
.
from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory factory = StopWordRemoverFactory() stopwords = factory.get_stop_words() print(stopwords)
.
['yang', 'untuk', 'pada', 'ke', 'para', 'namun', 'menurut', 'antara', 'dia', 'dua', 'ia', 'seperti', 'jika', 'jika', 'sehingga', 'kembali', 'dan', 'tidak', 'ini', 'karena', 'kepada', 'oleh', 'saat', 'harus', 'sementara', 'setelah', 'belum', 'kami', 'sekitar', 'bagi', 'serta', 'di', 'dari', 'telah', 'sebagai', 'masih', 'hal', 'ketika', 'adalah', 'itu', 'dalam', 'bisa', 'bahwa', 'atau', 'hanya', 'kita', 'dengan', 'akan', 'juga', 'ada', 'mereka', 'sudah', 'saya', 'terhadap', 'secara', 'agar', 'lain', 'anda', 'begitu', 'mengapa', 'kenapa', 'yaitu', 'yakni', 'daripada', 'itulah', 'lagi', 'maka', 'tentang', 'demi', 'dimana', 'kemana', 'pula', 'sambil', 'sebelum', 'sesudah', 'supaya', 'guna', 'kah', 'pun', 'sampai', 'sedangkan', 'selagi', 'sementara', 'tetapi', 'apakah', 'kecuali', 'sebab', 'selain', 'seolah', 'seraya', 'seterusnya', 'tanpa', 'agak', 'boleh', 'dapat', 'dsb', 'dst', 'dll', 'dahulu', 'dulunya', 'anu', 'demikian', 'tapi', 'ingin', 'juga', 'nggak', 'mari', 'nanti', 'melainkan', 'oh', 'ok', 'seharusnya', 'sebetulnya', 'setiap', 'setidaknya', 'sesuatu', 'pasti', 'saja', 'toh', 'ya', 'walau', 'tolong', 'tentu', 'amat', 'apalagi', 'bagaimanapun']
.
def stopword_text(tokens): cleaned_tokens = [] for token in tokens: if token not in stopwords: cleaned_tokens.append(token) return cleaned_tokens
.
df['stop'] = df['token'].apply(stopword_text) df.head()
.
.
Stemming
.
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory stem_factory = StemmerFactory() stemmer = stem_factory.create_stemmer()
.
def stemming_text(tokens): hasil = [stemmer.stem(token) for token in tokens] return hasil
.
df['stemmed'] = df['stop'].apply(stemming_text) df.head()
.
.
Viz
.
from nltk.probability import FreqDist
.
import matplotlib.pyplot as plt
.
all_tokens = [token for sublist in df['stemmed'] for token in sublist] freq_dist = FreqDist(all_tokens) print(freq_dist.most_common())
.
[('jadi', 1795), ('makan', 1683), ('', 1668), ('tubuh', 1531), ('sehat', 1485), ('lebih', 1467), ('orang', 1206), ('sebut', 1087), ('banyak', 1065), ('kandung', 994), ('sakit', 986), ('darah', 946), ('konsumsi', 928), ('laku', 903), ('minum', 862), ('buah', 826), ('milik', 793), ('satu', 790), ('sebab', 766), ('kata', 747), ('buat', 736), ('tingkat', 707), ('baik', 682), ('rasa', 669), ('tinggi', 668), ('hari', 659), ('bantu', 652), ('guna', 624), ('air', 599), ('tahun', 558), ('sangat', 556), ('hingga', 554), ('dapat', 552), ('lama', 526), ('rupa', 519), ('beberapa', 513), ('anak', 486), ('alami', 484), ('virus', 480), ('manfaat', 475), ('obat', 472), ('salah', 470), ('yg', 465), ('beri', 458), ('bagi', 436), ('tahu', 433), ('lalu', 432), ('indonesia', 431), ('vitamin', 426), ('jaga', 424), ('gula', 418), ('kurang', 410), ('biasa', 409), ('semua', 406), ('temu', 405), ('kerja', 400), ('sama', 398), ('hasil', 397), ('pisang', 393), ('rumah', 393), ('nyata', 392), ('masuk', 389), ('besar', 388), ('kanker', 387), ('perlu', 381), ('cara', 377), ('jumlah', 376), ('tak', 375), ('turun', 375), ('atas', 373), ('kamu', 371), ('kondisi', 370), ('tambah', 363), ('cegah', 354), ('bagai', 353), ('teliti', 352), ('dokter', 350), ('ikut', 347), ('mulai', 343), ('kasus', 337), ('mati', 335), ('asam', 332), ('mungkin', 329), ('hidup', 328), ('jenis', 327), ('ada', 326), ('c', 324), ('tepat', 323), ('covid', 316), ('butuh', 315), ('waktu', 315), ('manusia', 307), ('bukan', 305), ('masyarakat', 303), ('penting', 295), ('jelas', 293), ('tetap', 292), ('efek', 285), ('sel', 281), ('nutrisi', 280), ('diri', 276), ('jawab', 276), ('serat', 270), ('tidur', 270), ('jantung', 268), ('makin', 264), ('umum', 264), ('usia', 264), ('bentuk', 263), ('bahkan', 257), ('a', 256), ('jalan', 253), ('wanita', 253), ('baru', 252), ('sistem', 251), ('lihat', 250), ('vaksin', 250), ('keluar', 249), ('akhir', 248), ('cukup', 245), ('lain', 243), ('badan', 241), ('dr', 240), ('kulit', 240), ('nilai', 240), ('kena', 239), ('benar', 237), ('olahraga', 236), ('utama', 236), ('jangan', 234), ('tahan', 233), ('rendah', 232), ('bahaya', 231), ('masalah', 231), ('garam', 230), ('berat', 229), ('cerna', 228), ('apa', 228), ('proses', 227), ('tumbuh', 227), ('kalimat', 224), ('langsung', 221), ('coba', 221), ('b', 220), ('tentu', 217), ('mudah', 217), ('gejala', 216), ('sendiri', 216), ('kalori', 213), ('sering', 212), ('akibat', 212), ('sayur', 211), ('ganggu', 211), ('panas', 211), ('pilih', 210), ('kadar', 210), ('dunia', 208), ('risiko', 208), ('zat', 207), ('program', 206), ('derita', 206), ('ubah', 204), ('paling', 203), ('kemudian', 203), ('d', 201), ('kuat', 201), ('mampu', 200), ('hubung', 200), ('tempat', 199), ('pasien', 199), ('e', 199), ('lanjut', 197), ('fungsi', 196), ('rutin', 196), ('perintah', 194), ('hindar', 193), ('tekan', 193), ('kopi', 193), ('tangan', 191), ('kenal', 190), ('kait', 189), ('tinggal', 189), ('kali', 188), ('korban', 188), ('dasar', 185), ('nama', 185), ('kembang', 185), ('diet', 184), ('lemak', 184), ('negara', 184), ('usaha', 184), ('sedang', 183), ('cepat', 183), ('bakteri', 183), ('lengkap', 182), ('asupan', 181), ('sampah', 178), ('dampak', 177), ('gigi', 177), ('pengaruh', 173), ('diabetes', 172), ('luar', 172), ('keluarga', 171), ('olah', 170), ('ibu', 170), ('hilang', 169), ('ajar', 169), ('pernah', 169), ('tuju', 169), ('sulit', 166), ('dua', 166), ('buruk', 166), ('kecil', 165), ('seluruh', 165), ('bukti', 164), ('infeksi', 164), ('ingat', 163), ('aku', 163), ('tulis', 163), ('perut', 162), ('lemon', 162), ('terlalu', 160), ('timbul', 160), ('kutip', 159), ('antioksidan', 159), ('memang', 159), ('produksi', 158), ('kebal', 158), ('teh', 158), ('punya', 157), ('tunjuk', 157), ('bersih', 156), ('susu', 156), ('gizi', 155), ('bulan', 155), ('putih', 154), ('atur', 153), ('gram', 152), ('sesuai', 152), ('serang', 151), ('dingin', 151), ('daya', 150), ('bahan', 150), ('malam', 148), ('racun', 148), ('pria', 147), ('sembuh', 147), ('protein', 146), ('bunuh', 146), ('asal', 145), ('bawah', 145), ('pertama', 145), ('tulang', 145), ('of', 144), ('picu', 144), ('kaya', 143), ('nyeri', 143), ('hamil', 142), ('bawa', 142), ('ginjal', 141), ('bagaimana', 139), ('arti', 139), ('isi', 139), ('corona', 139), ('selalu', 138), ('nasi', 138), ('warga', 138), ('masak', 138), ('tentara', 138), ('terus', 137), ('sumber', 136), ('penuh', 134), ('aman', 134), ('beda', 134), ('otak', 132), ('meski', 131), ('anakanak', 131), ('periksa', 130), ('per', 130), ('pasti', 129), ('muncul', 128), ('pasang', 128), ('otot', 127), ('pola', 127), ('tanda', 127), ('es', 127), ('bebas', 126), ('usus', 126), ('batas', 126), ('perhati', 125), ('ukur', 125), ('dukung', 125), ('awal', 125), ('informasi', 125), ('bahas', 124), ('hormon', 124), ('hitam', 124), ('ahli', 123), ('peran', 123), ('sebar', 123), ('jam', 121), ('dekat', 121), ('batu', 121), ('sosial', 121), ('kalau', 121), ('khusus', 120), ('keras', 120), ('minggu', 120), ('menteri', 120), ('ambil', 119), ('mana', 119), ('pandemi', 119), ('campur', 119), ('sedikit', 116), ('ungkap', 116), ('suhu', 116), ('sirsak', 116), ('apel', 115), ('aktivitas', 115), ('tiga', 114), ('persen', 114), ('sekali', 114), ('buluh', 114), ('teks', 114), ('sekarang', 114), ('giat', 114), ('kualitas', 113), ('panjang', 113), ('hati', 112), ('maupun', 112), ('masa', 112), ('serta', 112), ('merah', 111), ('tes', 111), ('tengah', 111), ('kepala', 111), ('hangat', 111), ('soal', 110), ('sejak', 110), ('moga', 109), ('energi', 109), ('latih', 108), ('baca', 108), ('lawan', 107), ('contoh', 107), ('pikir', 106), ('sekolah', 106), ('pihak', 106), ('kartu', 106), ('bayi', 105), ('daging', 105), ('pakai', 105), ('teman', 105), ('minyak', 104), ('suka', 104), ('lapor', 104), ('lambung', 103), ('buka', 103), ('telur', 102), ('banding', 102), ('mata', 102), ('tugas', 102), ('data', 102), ('malaysia', 102), ('lindung', 101), ('terima', 101), ('soda', 101), ('bau', 101), ('segera', 100), ('percaya', 100), ('tutup', 100), ('kucing', 100), ('studi', 99), ('ikan', 99), ('saksi', 99), ('doe', 98), ('alam', 97), ('who', 97), ('lahir', 97), ('mental', 97), ('miskin', 97), ('tua', 96), ('kini', 96), ('biaya', 96), ('padahal', 96), ('wkwk', 96), ('beli', 96), ('alkohol', 96), ('hukum', 96), ('organ', 95), ('normal', 94), ('misal', 94), ('rp', 94), ('selamat', 94), ('tapioka', 94), ('segar', 92), ('main', 92), ('uang', 92), ('prakerja', 92), ('fisik', 91), ('capai', 91), ('anggota', 91), ('lewat', 91), ('bercak', 91), ('sukses', 91), ('besi', 90), ('apabila', 90), ('asa', 90), ('jus', 90), ('bila', 90), ('kota', 90), ('desa', 90), ('rusa', 89), ('duduk', 89), ('bicara', 89), ('medis', 89), ('rusak', 89), ('bawang', 89), ('aborsi', 89), ('suatu', 88), ('detikers', 88), ('sadar', 88), ('babi', 88), ('donor', 88), ('fluoride', 88), ('rencana', 87), ('produk', 87), ('tanya', 87), ('alir', 87), ('kacang', 86), ('samping', 86), ('sendi', 86), ('selsel', 86), ('sedia', 86), ('perempuan', 86), ('rumput', 86), ('pagi', 85), ('alas', 85), ('saran', 85), ('rokok', 85), ('awat', 85), ('manis', 85), ('senyawa', 84), ('daun', 84), ('paruparu', 84), ('camat', 84), ('napas', 84), ('militer', 84), ('wilfrida', 84), ('dengar', 83), ('empat', 83), ('seksual', 83), ('antibodi', 83), ('khawatir', 82), ('menit', 82), ('jakarta', 82), ('enak', 82), ('naik', 82), ('minta', 82), ('mulut', 81), ('mandi', 81), ('warna', 80), ('gagal', 80), ('anggap', 80), ('paragraf', 80), ('berita', 80), ('wedang', 80), ('lansir', 79), ('terang', 79), ('jauh', 79), ('dalam', 79), ('artikel', 78), ('cair', 78), ('stunting', 78), ('catat', 78), ('reda', 78), ('paham', 78), ('tular', 78), ('operasi', 78), ('mie', 78), ('sarap', 77), ('uji', 77), ('lingkung', 77), ('negeri', 77), ('sifilis', 77), ('alat', 76), ('siap', 76), ('didik', 76), ('gerak', 76), ('upaya', 76), ('cocok', 75), ('matang', 75), ('stroberi', 75), ('idap', 75), ('video', 75), ('john', 75), ('gas', 75), ('muda', 74), ('imbang', 74), ('buang', 74), ('belanja', 74), ('bangun', 74), ('positif', 74), ('upa', 74), ('menang', 74), ('wajib', 74), ('mineral', 73), ('lancar', 73), ('simak', 73), ('adang', 73), ('tarik', 73), ('henti', 73), ('harap', 73), ('takut', 73), ('kalium', 72), ('aroma', 72), ('aktif', 72), ('sudan', 72), ('nikmat', 71), ('lepas', 71), ('daerah', 71), ('salur', 71), ('karbohidrat', 70), ('syarat', 70), ('kerap', 70), ('tanam', 70), ('kafein', 70), ('bandung', 70), ('demam', 70), ('nya', 70), ('laman', 69), ('dorong', 69), ('bidang', 69), ('sifat', 69), ('prabowo', 69), ('hewan', 69), ('umur', 69), ('rebus', 68), ('justru', 68), ('efektif', 68), ('cari', 68), ('potasium', 68), ('ekonomi', 68), ('sayang', 68), ('depan', 68), ('ereksi', 68), ('anjur', 67), ('health', 67), ('sempat', 67), ('dewasa', 67), ('khas', 67), ('udara', 67), ('jawa', 67), ('siswa', 67), ('imun', 67), ('tindak', 67), ('vagina', 67), ('jepang', 66), ('cinta', 66), ('teknologi', 66), ('media', 66), ('cerita', 66), ('lemah', 66), ('ilmuwan', 66), ('exxonmobil', 66), ('kan', 65), ('faktor', 65), ('oksigen', 65), ('letak', 65), ('amerika', 65), ('hampir', 65), ('buahbuahan', 64), ('balik', 64), ('biar', 64), ('magnesium', 64), ('layan', 64), ('ribu', 64), ('oleh', 64), ('vaksinasi', 64), ('hipertensi', 64), ('tumpang', 64), ('ganti', 63), ('kendali', 63), ('tipe', 63), ('bijak', 63), ('cenderung', 63), ('sapi', 63), ('gugat', 63), ('menu', 62), ('reaksi', 62), ('seks', 62), ('pukul', 62), ('kontrol', 62), ('nafas', 62), ('tim', 62), ('simpul', 62), ('man', 62), ('si', 62), ('masker', 62), ('nomor', 61), ('belakang', 61), ('negatif', 61), ('sampai', 61), ('lomba', 61), ('belum', 61), ('i', 61), ('gandum', 61), ('kenyang', 60), ('janin', 60), ('karena', 60), ('kemas', 60), ('rekomendasi', 60), ('ph', 60), ('datang', 60), ('new', 60), ('siapa', 60), ('tranq', 60), ('puter', 60), ('adil', 60), ('makgeolli', 60), ('info', 60), ('ulang', 59), ('game', 59), ('korupsi', 59), ('lima', 59), ('hijau', 58), ('lambat', 58), ('jeruk', 58), ('adapun', 58), ('selesai', 58), ('barat', 58), ('semester', 58), ('luka', 58), ('pneumonia', 58), ('favorit', 57), ('nasional', 57), ('tegas', 57), ('stres', 57), ('khasiat', 57), ('serap', 57), ('nafsu', 57), ('roti', 57), ('jaring', 57), ('wabah', 57), ('mau', 57), ('rambut', 57), ('lupa', 56), ('tunggu', 56), ('juta', 56), ('kembali', 56), ('kolesterol', 56), ('ruang', 56), ('komunitas', 56), ('dgn', 56), ('utuh', 55), ('fokus', 55), ('ancam', 55), ('barang', 55), ('papar', 55), ('the', 55), ('ii', 55), ('lokasi', 55), ('ri', 55), ('gantung', 54), ('fasilitas', 54), ('jarang', 54), ('cipta', 54), ('kosong', 54), ('muka', 54), ('tahap', 54), ('duga', 54), ('resmi', 54), ('serikat', 54), ('akun', 54), ('asi', 54), ('kelompok', 53), ('glukosa', 53), ('ekor', 53), ('suami', 53), ('hitung', 52), ('goreng', 52), ('subur', 52), ('terapi', 52), ('terap', 52), ('juri', 52), ('cuci', 52), ('transportasi', 52), ('kasih', 52), ('anti', 52), ('arah', 52), ('bengkak', 52), ('selasa', 52), 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('sihpemerintah', 1), ('kau', 1), ('kiamat', 1), ('suci', 1), ('penghunitidak', 1), ('dimasjidkok', 1), ('nyuruh', 1), ('sawtidak', 1), ('anjing', 1), ('berfikir', 1), ('sebernarnya', 1), ('mataberarti', 1), ('sekongkol', 1), ('jin', 1), ('kitayang', 1), ('nyatidak', 1), ('makanyamikir duluyapakjokowi', 1), ('otaktolongdipakaibenar', 1), ('tangerine', 1), ('allhamdullah', 1), ('keluaragadan', 1), ('bisatdk', 1), ('ahirnya', 1), ('lautsetiap', 1), ('wktu', 1), ('enteng', 1), ('cekup', 1), ('alhamdlllh', 1), ('cofid', 1), ('sblmya', 1), ('km', 1), ('bahwasanya', 1), ('pecaya', 1), ('lamhanya', 1), ('kepadanyaapa', 1), ('nb', 1), ('hoaxini', 1), ('imigran', 1), ('pakarpakar', 1), ('tekak', 1), ('medsos', 1), ('viruscorona', 1), ('oey', 1), ('him', 1), ('toan', 1), ('butir', 1), ('istirahattidur', 1), ('lt', 1), ('rawisabang', 1), ('jokorawisabang', 1), ('xinhua', 1), ('wenliang', 1), ('usul', 1), ('theophylline', 1), ('stimulan', 1), ('methylxanthines', 1), ('theophilin', 1), ('otomatis', 1), ('siur', 1), ('gaduh', 1), ('dikembangbiakan', 1), ('temperatursuhu', 1), ('cawan', 1), ('mould', 1), ('tapi apa', 1), ('pengembangbiakannya', 1), ('bombardir', 1), ('nalarlogika', 1), ('varia', 1), ('mahk', 1), ('ota', 1), ('antena', 1), ('cginya', 1), ('generated', 1), ('images', 1), ('desinfektan', 1), ('screen', 1), ('infrm', 1), ('ketemu', 1), ('koq', 1), ('nah media', 1), ('risolnya', 1), ('ba', 1), ('gaimana', 1), ('statement', 1), ('menkes', 1), ('gonjang', 1), ('ganjing', 1), ('risol', 1), ('hygiene', 1), ('rb', 1), ('kuras', 1), ('sofa', 1), ('nyetok', 1), ('kek cincin', 1), ('kek gelang', 1), ('kek kalung', 1), ('simple', 1), ('seabreg', 1), ('grade', 1), ('kacamata', 1), ('mikroskop', 1), ('ngaco', 1), ('chemicals', 1), ('rokoktembakau', 1), ('paper', 1), ('kloset', 1), ('washtafel', 1), ('ingus', 1), ('menyaringmenangkapmengurung', 1), ('dahakingus', 1), ('lagipula', 1), ('hayo', 1), ('looo', 1), ('dongkrak', 1), ('cukabayclin', 1), ('liang', 1), ('lega ruan', 1), ('ebih', 1), ('ta', 1), ('npa', 1), ('merkuri', 1), ('thimerosal', 1), ('soniania', 1), ('reach', 1), ('anytime', 1), ('ask', 1), ('something', 1), ('about', 1), ('your', 1), ('paranoid', 1), ('hysteria', 1), ('dompleng', 1), ('eyes', 1), ('darkness', 1), ('koontz', 1), ('seller', 1), ('senuah', 1), ('ncov', 1), ('hubei', 1), ('rumpun', 1), ('mers', 1), ('modalitas', 1), ('fusi', 1), ('penyakitpenyakit', 1), ('terinveksi', 1), ('wowww', 1), ('ayooo', 1), ('leluhur', 1), ('peace', 1), ('note', 1), ('laila', 1), ('ahmadi', 1), ('zanjan', 1), ('covd', 1), ('kit', 1), ('diagnostik', 1), ('beijing', 1), ('karboksilat', 1), ('kokain', 1), ('siasat', 1), ('ikutin', 1), ('tks', 1), ('albaqarah', 1), ('puruk', 1), ('taipan', 1), ('sperti', 1), ('sarden', 1), ('alfamart', 1), ('indomaret', 1), ('pruduksi', 1), ('carona', 1), ('tpi', 1), ('yogya', 1), ('dbawa', 1), ('rongent', 1), ('keluargateman', 1), ('kretek', 1), ('drh', 1), ('puti', 1), ('keduasetelah', 1), ('jiplak', 1), ('aa', 1), ('berdasarkanm', 1), ('tnh', 1), ('htm', 1), ('sean', 1), ('oitiap', 1), ('hri', 1), ('msk', 1), ('duniakangkung', 1), ('malapetaka', 1), ('sajiansedapcom', 1), ('terlau', 1), ('tribunstylecom', 1), ('efektifitasnya', 1), ('kasein', 1), ('penyalit', 1), ('gridnetworkjuara', 1), ('bright', 1), ('farah', 1), ('blacksmallsquaresiapkan', 1), ('blacksmallsquareisi', 1), ('blacksmallsquaremasukkan', 1), ('blacksmallsquarebiarkan', 1), ('blacksmallsquaresetelah', 1), ('inbox', 1), ('aq', 1), ('jabang', 1), ('boldsky', 1), ('tulsi', 1), ('jangant', 1), ('bsk', 1), ('smua', 1), ('bayibayi', 1), ('presentase', 1), ('merahmerah', 1), ('antinganting', 1), ('kejadiankejadian', 1), ('curi', 1), ('mensturasi', 1), ('pijak', 1), ('dimanamana', 1), ('haluslembut', 1), ('sahaja', 1), ('bergerakgerak', 1), ('semogamembantu', 1), ('kpda', 1), ('saudarasaudari', 1), ('wag', 1), ('entomolog', 1), ('sava', 1), ('plug', 1), ('venasaluran', 1), ('bypass', 1), ('khabar', 1), ('engah engah', 1), ('stent', 1), ('pass', 1), ('plaque', 1), ('calsium', 1), ('cardiologist', 1), ('internist', 1), ('cardiologi', 1), ('medika', 1), ('spjp', 1), ('emeritus', 1), ('kardiologi', 1), ('fkui', 1), ('graduate', 1), ('course', 1), ('erasmus', 1), ('roterdam', 1), ('fellow', 1), ('kapi', 1), ('kardioserebrovaskular', 1), ('ikki', 1), ('indonesian', 1), ('cardiovacular', 1), ('ics', 1), ('principal', 1), ('investigator', 1), ('multinational', 1), ('study', 1), ('dibacaa', 1), ('makkkkkk', 1), ('mematika', 1), ('menghancurk', 1), ('memba', 1), ('luarbiasa', 1), ('masal', 1), ('des', 1), ('byk', 1), ('crispy', 1), ('monggo', 1), ('knp', 1), ('cecer', 1), ('serak', 1), ('limfe', 1), ('dihimbau', 1), ('ludah', 1), ('rekreasi', 1), ('daging mari', 1), ('subhanalloh', 1), ('ter', 1), ('salaaam', 1), ('england', 1), ('lirik', 1), ('press', 1), ('gerontologist', 1), ('karen', 1), ('weatherby', 1), ('kec', 1), ('dukun', 1), ('muntilan', 1), ('sampaikn', 1), ('orangtua', 1), ('paskibraka', 1), ('enrekang', 1), ('pangkat', 1), ('ngasih', 1), ('jaket', 1), ('diabetesh', 1), ('zara', 1), ('myom', 1), ('idam', 1), ('jenak', 1), ('yu', 1), ('zongxian', 1), ('mudahmudahan', 1), ('pfoa', 1), ('satwa', 1), ('metal', 1), ('pfda', 1), ('ptfe', 1), ('polytetrafluoroethylene', 1), ('amplasambril', 1), ('almunium', 1), ('sebaikknya', 1), ('stainlees', 1), ('bisa', 1), ('mkn', 1), ('lg', 1), ('siklam', 1), ('immunisasi', 1), ('zionist yg', 1), ('penuuuh', 1), ('suaranasional', 1), ('httpbitlyvrysyp', 1), ('facebookcomgroups', 1), ('nungky', 1), ('herbalis', 1), ('facebookcomnungkyherbalis', 1), ('turnbackhoaxid', 1), ('mar', 1), ('httpbitlykzyuqs', 1), ('httpbitlybjrmx', 1), ('nia', 1), ('deviyana', 1), ('fotofreeimages', 1), ('blackberry', 1), ('messanger', 1), ('cma', 1), ('meninggaltetapi', 1), ('coklatterjadilah', 1), ('hatihatijika', 1), ('pastelah', 1), ('rebo', 1), ('sulianti', 1), ('ros', 1), ('httpbitlynndpsf', 1), ('ajeng', 1), ('anastasia', 1), ('kinanti', 1), ('paste', 1), ('mainmain', 1), ('investigasi', 1), ('leona', 1), ('djajadi', 1), ('mnd', 1), ('sintetik', 1), ('reaksireaksi', 1), ('bahasabahasa', 1), ('sertamerta', 1), ('ajgup', 1), ('httpbitlynngjx', 1), ('giovani', 1), ('prasasti', 1), ('screenshot', 1), ('profdrir', 1), ('rokhmin', 1), ('dahuri', 1), ('bogor', 1), ('luat', 1), ('inderanya', 1), ('lawas', 1), ('wkt', 1), ('msih', 1), ('pkai', 1), ('bkan', 1), ('android', 1), ('sebelummnya', 1), ('httpswwwinstagramcompbktztpofefl', 1), ('hoaxkesehatan', 1), ('antihoax', 1), ('beritakanyangbenar', 1), ('sehatnegerikukemkesgoid', 1), ('flickr', 1), ('hours', 1), ('kemenkesfact', 1), ('kemenkestips', 1), ('jejaring', 1), ('dra', 1), ('kustantinah', 1), ('apt', 1), ('mappsc', 1), ('httpbitlynillqi', 1), ('meyta', 1), ('syafitri', 1), ('facebookcommeytasyafitri', 1), ('agu', 1), ('sep', 1), ('httpbitlymnnzuu', 1), ('ms', 1), ('bharu', 1), ('hofahmi', 1), ('markus', 1), ('junianto', 1), ('sihaloho', 1), ('pdi', 1), ('diah', 1), ('mobilisasi', 1), ('lejit', 1), ('moratorium', 1), ('rafidzi', 1), ('mpr', 1), ('dhanusubroto', 1), ('jokowijusuf', 1), ('kalla', 1), ('pencapresan', 1), ('kompensasi', 1), ('hakhak', 1), ('masalahmasalah', 1), ('pull', 1), ('push', 1), ('realitas', 1), ('realistis', 1), ('normatif', 1), ('httpbitlykxtj', 1), ('fajar', 1), ('ilustrasithinkstock', 1), ('siar', 1), ('jaksa', 1), ('rayu', 1), ('putrajaya', 1), ('genap', 1), ('sindikat', 1), ('kuala', 1), ('lumpur', 1), ('ong', 1), ('poempida', 1), ('dapil', 1), ('mboeik', 1), ('komnas', 1), ('uskup', 1), ('atambua', 1), ('vivat', 1), ('paul', 1), ('melanie', 1), ('subono', 1), ('koordiantif', 1), ('kualalumpur', 1), ('goris', 1), ('untukmendesak', 1), ('pitalokadan', 1), ('najibrazak', 1), ('change', 1), ('usman', 1), ('kyai', 1), ('maman', 1), ('imanul', 1), ('haq', 1), ('susetyo', 1), ('andar', 1), ('nubowo', 1), ('sby', 1), ('mention', 1), ('sbyudhoyono', 1), ('hi', 1), ('soekarno', 1), ('hatta', 1), ('lawyer', 1), ('dialogue', 1), ('migration', 1), ('developmentdi', 1), ('women', 1), ('conference', 1), ('rdpu', 1), ('johor', 1), ('fajdra', 1), ('httpbitlymsdaq', 1), ('subiantokompascomsabrina', 1), ('halim', 1), ('insya', 1), ('komando', 1), ('kopassus', 1), ('buntut', 1), ('yeap', 1), ('seok', 1), ('pen', 1), ('nal', 1), ('kanun', 1), ('keseksaan', 1), ('raftfizi', 1), ('editor', 1), ('priyo', 1), ('jatmiko', 1), ('httpbitlyvcywk', 1), ('agniya', 1), ('nurul', 1), ('fadhilah', 1), ('facebookcomiffahmusyaroffah', 1), ('santri', 1), ('pesantren', 1), ('gayam', 1), ('muntahmuntah', 1), ('outbreak', 1), ('response', 1), ('immunization', 1), ('ori', 1), ('pascaimunisasi', 1), ('httpbitlyvgftxc', 1), ('ipah', 1), ('latifah', 1), ('facebookcomifahnotlatifah', 1), ('bingkai', 1), ('embrio', 1), ('stem', 1), ('httpbitlyvhsynq', 1), ('inews', 1), ('special', 1), ('selmediated', 1), ('fragmen', 1), ('vertebrata', 1), ('netralisasi', 1), ('isotipe', 1), ('httpbitlyahtmkb', 1), ('how', 1), ('ribonucleic', 1), ('deoxyribonucleic', 1), ('host', 1), ('multivalen', 1), ('musyawarah', 1), ('galon', 1), ('dramatis', 1), ('sentimeter', 1), ('kubik', 1), ('freezerdipertahankan', 1), ('grafik', 1), ('tangas', 1), ('pramanufaktur', 1), ('katup', 1), ('koneksi', 1), ('tripsin', 1), ('katalis', 1), ('mikroskopik', 1), ('disentrifugasi', 1), ('manik', 1), ('ru', 1), ('virulensi', 1), ('goldilocks', 1), ('atenuasi', 1), ('aktual', 1), ('autoclave', 1), ('elevator', 1), ('gaun', 1), ('tyvek', 1), ('sarung', 1), ('boot', 1), ('ruangruang', 1), ('rancang', 1), ('bukubuku', 1), ('dulbecco', 1), ('renato', 1), ('harold', 1), ('ginsberg', 1), ('edisi', 1), ('jb', 1), ('lippincott', 1), ('plotkin', 1), ('stanley', 1), ('mortimer', 1), ('jr', 1), ('eds', 1), ('saunders', 1), ('berlow', 1), ('translate', 1), ('chrome', 1), ('extension', 1), ('english', 1), ('httpbitlyoccr', 1), ('ncbi', 1), ('dasardasar', 1), ('angela', 1), ('clem', 1), ('pmc', 1), ('abstrak', 1), ('cellmediated', 1), ('kerak', 1), ('wortley', 1), ('resistansi', 1), ('protektif', 1), ('pelekat', 1), ('silia', 1), ('hidrolitik', 1), ('peptidoglycan', 1), ('lisis', 1), ('mukosa', 1), ('subclass', 1), ('properdin', 1), ('cb', 1), ('lektin', 1), ('mannosebinding', 1), ('kardinal', 1), ('germline', 1), ('variabilitas', 1), ('creaktif', 1), ('tol', 1), ('tlrs', 1), ('ctype', 1), ('nod', 1), ('mx', 1), ('pamp', 1), ('lps', 1), ('endotoksin', 1), ('peptidoglikan', 1), ('lipoprotein', 1), ('hypomethylated', 1), ('cpg', 1), ('untai', 1), ('ganda', 1), ('flagella', 1), ('granulositik', 1), ('presentasi', 1), ('antimikroba', 1), ('sitotoksik', 1), ('diferensiasi', 1), ('limfatik', 1), ('antigenantibodi', 1), ('granulocytic', 1), ('histamin', 1), ('naif', 1), ('antigenpresenting', 1), ('lipopolisakarida', 1), ('dekstran', 1), ('determinan', 1), ('antigenantigen', 1), ('pensinyalan', 1), ('hypermutation', 1), ('somatik', 1), ('klonklon', 1), ('spesifisitas', 1), ('klonal', 1), ('penetralisir', 1), ('eksposur', 1), ('intraseluler', 1), ('timus', 1), ('coreseptor', 1), ('tsitotoksik', 1), ('memilihmilih', 1), ('jawaposcom', 1), ('httpswwwfacebookcomkhaisarandriposts', 1), ('sajakalau', 1), ('milih', 1), ('alat alat', 1), ('warnawarna', 1), ('autis', 1), ('tantrum', 1), ('mbak', 1), ('indryesti', 1), ('wukir', 1), ('kriswanto', 1), ('sutil', 1), ('seereeem', 1), ('blimbing', 1), ('asem', 1), ('waah', 1), ('acar', 1), ('ahh', 1), ('kunir', 1), ('waa', 1), ('hiii rasah', 1), ('smoga', 1), ('brmanfaat', 1), ('httpswwwjawaposcomreadbikinresahpenggunapancialuminium', 1), ('httpsfoodidntimescomdiningguidestellacaramudahmengetahuiesbatuyangkamumakanitumatangataumentahfull', 1), ('renyah', 1), ('cuek', 1), ('molekulmolekulnya', 1), ('keruh', 1), ('muntaber', 1), ('telanjang', 1), ('tanyatanya', 1), ('diposting', 1), ('hatihatilah', 1), ('account', 1), ('lanny', 1), ('photo', 1), ('ketidaksamaan', 1), ('facebooknya', 1), ('ngalamin', 1), ('dipasarwarung', 1), ('gant', 1), ('hahaha', 1), ('polos', 1), ('indonmie', 1), ('masih', 1), ('pepet', 1), ('lapartetapi', 1), ('belikan', 1), ('product', 1), ('stemp', 1), ('kecoh', 1), ('higienis', 1), ('saudarasaudara', 1), ('idisaat', 1), ('blkgjangan', 1), ('produkextra', 1), ('kirantikratingdaenghemaviton', 1), ('hormovitonmarimas', 1), ('frutillosegar', 1), ('icesegar', 1), ('inacogatoradenabati', 1), ('drhismuhadi', 1), ('molasses', 1), ('dance', 1), ('httpphkillscancercom', 1), ('jai', 1), ('pesona', 1), ('malmal', 1), ('asambasa', 1), ('meningatkan', 1), ('molasse', 1), ('nyala', 1), ('ipar', 1), ('perexmpuan', 1)]
.
freq_dist.plot(30,cumulative=False) plt.show()
.
.
Simpan Dataset Bersih
.
df.to_csv(r"C:\Users\GK3v\Documents\Python\NaiveBayesTfidf\Preprocessing\data_clean.csv", index=False)
.
data = pd.read_csv(r"C:\Users\GK3v\Documents\Python\NaiveBayesTfidf\Preprocessing\data_clean.csv")
.
data.head(20)
.
.
Metode Bayes
.
web_hoax = pd.read_excel(r"C:\Users\GK3v\Documents\Python\NaiveBayesTfidf\Preprocessing\databeritabaru.xlsx", header=0)
.
print(web_hoax.shape)
.
(667, 2)
.
web_hoax.head()
.
| Label | Article | |
|---|---|---|
| 0 | ham | Makanan rendah kalori cocok dikonsumsi untuk k… |
| 1 | ham | Sayuran merupakan makanan terbaik untuk keseha… |
| 2 | ham | Pada penderita diabetes, cek gula darah dilaku… |
| 3 | ham | Pisang merupakan salah satu jenis buah yang me… |
| 4 | ham | Kepala Badan Kependudukan dan Keluarga Berenca… |
.
web_hoax[‘Label’].value_counts(normalize=True)
.
Label ham 0.601199 hoax 0.398801 Name: proportion, dtype: float64
.
# Mengacak dataset data_randomized = web_hoax.sample(frac=1, random_state=1)
.
# Menghitung index untuk pemisahan training_test_index = round(len(data_randomized) * 0.8)
.
# Membagi menjadi training sets and test sets training_set_final = data_randomized[:training_test_index].reset_index(drop=True) test_set_final = data_randomized[training_test_index:].reset_index(drop=True)
.
print(training_set_final.shape) print(test_set_final.shape)
.
(534, 2) (133, 2)
.
training_set_final['Label'].value_counts(normalize=True)
.
Label ham 0.608614 hoax 0.391386 Name: proportion, dtype: float64
.
test_set_final['Label'].value_counts(normalize=True)
.
Label ham 0.571429 hoax 0.428571 Name: proportion, dtype: float64
.
# Sebelum membersihkan training_set_final.head(3)
.
.
# Setelah membersihkan
training_set_final['Article'] = training_set_final['Article'].str.replace('\W', ' ') # Menghapus Tanda Baca
training_set_final['Article'] = training_set_final['Article'].str.lower()
training_set_final.head(3)
.
.
Vocabulary
.
import pandas as pd artikel_spam = pd.read_csv(r"C:\Users\GK3v\Documents\Python\NaiveBayesTfidf\Preprocessing\data_clean.csv", header=0)
.
print(artikel_spam.shape)
.
artikel_spam.head()
.
.
training_set_clean = pd.concat([artikel_spam['stemmed'], word_counts], axis=1) training_set_clean.head()
.
.
artikel_spam['stemmed'] = artikel_spam['stemmed'].str.split() vocabulary = [] for article in artikel_spam['stemmed']: for word in article: vocabulary.append(word) vocabulary = list(set(vocabulary))
.
len(vocabulary)
.
11865
.
word_counts_per_article = {unique_word: [0] * len(artikel_spam['stemmed']) for unique_word in vocabulary}
for index, article in enumerate(artikel_spam['stemmed']):
for word in article:
word_counts_per_article[word][index] += 1
.
word_counts = pd.DataFrame(word_counts_per_article) word_counts.head()
.
.
artikel_spam['stemmed'] = artikel_spam['stemmed'].str.split() vocabulary = [] for article in artikel_spam['stemmed']: for word in article: vocabulary.append(word) vocabulary = list(set(vocabulary))
.
len(vocabulary)
.
11865
.
word_counts_per_article = {unique_word: [0] * len(artikel_spam['stemmed']) for unique_word in vocabulary}
for index, article in enumerate(artikel_spam['stemmed']):
for word in article:
word_counts_per_article[word][index] += 1
.
word_counts = pd.DataFrame(word_counts_per_article) word_counts.head()
.
.
training_set_clean_final = pd.concat([training_set_final, word_counts], axis=1) training_set_clean_final.head()
.
.
# Isolating spam and ham messages first hoax_messages = training_set_clean_final[training_set_clean_final['Label'] == 'hoax'] ham_messages = training_set_clean_final[training_set_clean_final['Label'] == 'ham'] # P(Spam) and P(Ham) p_hoax = len(hoax_messages) / len(training_set_clean_final) p_ham = len(ham_messages) / len(training_set_clean_final) # N_Spam n_words_per_hoax_message = hoax_messages['Article'].apply(len) n_hoax = n_words_per_hoax_message.sum() # N_Ham n_words_per_ham_message = ham_messages['Article'].apply(len) n_ham = n_words_per_ham_message.sum() # N_Vocabulary n_vocabulary = len(vocabulary) # Laplace smoothing alpha = 1
.
# Initiate parameters
parameters_hoax = {unique_word:0 for unique_word in vocabulary}
parameters_ham = {unique_word:0 for unique_word in vocabulary}
# Calculate parameters
for word in vocabulary:
n_word_given_hoax = hoax_messages[word].sum() # hoax_messages already defined
p_word_given_hoax = (n_word_given_hoax + alpha) / (n_hoax + alpha*n_vocabulary)
parameters_hoax[word] = p_word_given_hoax
n_word_given_ham = ham_messages[word].sum() # ham_messages already defined
p_word_given_ham = (n_word_given_ham + alpha) / (n_ham + alpha*n_vocabulary)
parameters_ham[word] = p_word_given_ham
.
import re
def classify(message):
'''
message: a string
'''
message = re.sub('\W', ' ', message)
message = message.lower().split()
p_hoax_given_message = p_hoax
p_ham_given_message = p_ham
for word in message:
if word in parameters_hoax:
p_hoax_given_message *= parameters_hoax[word]
if word in parameters_ham:
p_ham_given_message *= parameters_ham[word]
print('P(Hoax|message):', p_hoax_given_message)
print('P(Ham|message):', p_ham_given_message)
if p_ham_given_message > p_hoax_given_message:
print('Label: Ham')
elif p_ham_given_message < p_hoax_given_message:
print('Label: Spam')
else:
print('Probabilitas yang sama, mintalah manusia mengklasifikasikannya!')
.
Tes Input Artikel
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classify('Makanan rendah kalori cocok dikonsumsi untuk kamu yang sedang menjalani program diet.')
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P(Hoax|message): 0.31334332833583206 P(Ham|message): 0.487256371814093 Label: Ham
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classify('Kedengarannya bagus, Tom, sampai jumpa di sana')
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P(Hoax|message): 0.31334332833583206 P(Ham|message): 0.487256371814093 Label: Ham
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def classify_test_set(message):
'''
message: a string
'''
message = re.sub('\W', ' ', message)
message = message.lower().split()
p_hoax_given_message = p_hoax
p_ham_given_message = p_ham
for word in message:
if word in parameters_hoax:
p_hoax_given_message *= parameters_hoax[word]
if word in parameters_ham:
p_ham_given_message *= parameters_ham[word]
if p_ham_given_message > p_hoax_given_message:
return 'ham'
elif p_hoax_given_message > p_ham_given_message:
return 'hoax'
else:
return 'membutuhkan klasifikasi manusia'
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test_set_final['Diprediksi'] = test_set_final['Article'].apply(classify_test_set) test_set_final.head(20)
.
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correct = 0
total = test_set_final.shape[0]
for row in test_set_final.iterrows():
row = row[1]
if row['Label'] == row['Diprediksi']:
correct += 1
print('Benar - Ham :', correct)
print('Tidak Benar - Hoax :', total - correct)
print('Tingkat Keakuratan Cek dalam % :', correct/total)
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Benar - Ham : 71 Tidak Benar - Hoax : 62 Tingkat Keakuratan Cek dalam % : 0.5338345864661654
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