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      "keywords": "Phishing URL, Multinomial Naïve Bayes, Bag of Words, TF-IDF,\r\nSMOTE.",
      "date": "2026-01-23",
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      "abstract": "Phishing merupakan salah satu bentuk serangan siber yang memanfaatkan\r\nURL palsu untuk menipu pengguna agar memberikan informasi sensitif. Seiring\r\nmeningkatnya jumlah serangan phishing, diperlukan metode deteksi otomatis yang\r\nefektif dan efisien. Penelitian ini bertujuan untuk membangun dan mengevaluasi\r\nmodel deteksi phishing URL menggunakan algoritma Multinomial Naïve Bayes\r\ndengan dua metode representasi teks, yaitu Bag of Words (BoW) dan Term\r\nFrequency–Inverse Document Frequency (TF-IDF).\r\nDataset yang digunakan terdiri dari beberapa kelas URL, yaitu benign,\r\nphishing, malware, dan defacement, yang memiliki distribusi data tidak seimbang.\r\nUntuk mengatasi permasalahan tersebut, diterapkan teknik Synthetic Minority\r\nOver-sampling Technique (SMOTE) pada data latih. Proses penelitian meliputi\r\ntahapan preprocessing teks, representasi fitur menggunakan character n-gram,\r\npelatihan model, serta evaluasi menggunakan metrik accuracy, precision, recall,\r\ndan F1-score. Selain itu, dilakukan 5-Fold Cross Validation untuk mengukur\r\nstabilitas dan kemampuan generalisasi model.\r\nHasil pengujian menunjukkan bahwa kedua metode representasi teks\r\nmampu menghasilkan performa klasifikasi yang baik. Representasi BoW\r\nmenghasilkan accuracy sebesar (78,20%) dan F1-score sebesar (78,59%),\r\nsedangkan TF-IDF memperoleh accuracy sebesar (77,90%) dan F1-score sebesar\r\n(79,16%). Dari sisi stabilitas, hasil validasi menunjukkan nilai CV-Mean yang\r\nkonsisten pada kedua metode. Selain itu, BoW memiliki waktu komputasi\r\nvektorisasi yang lebih rendah dibandingkan TF-IDF, sehingga lebih efisien dalam\r\npemrosesan data skala besar. Berdasarkan evaluasi kinerja, stabilitas model, dan\r\nefisiensi waktu komputasi, model Multinomial Naïve Bayes dengan representasi\r\nBoW dan penerapan SMOTE dipilih sebagai model terbaik dalam penelitian ini.",
      "lastmod": "2026-07-02 04:02:09",
      "eprint_status": "archive",
      "title": "MULTINOMIAL NAÏVE BAYES UNTUK DETEKSI PHISHING\r\nURL DENGAN PERBANDINGAN REPRESENTASI FITUR\r\nBAG OF WORDS DAN TF-IDF",
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