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      "abstract": "Perubahan kebijakan platform X pada Juni 2024 yang mengizinkan konten dewasa\r\nsecara konsensual telah menciptakan celah keamanan siber baru. Pelaku kejahatan\r\nsiber sering memanfaatkan konten tersebut sebagai umpan (bait) untuk\r\nmenyebarkan tautan phishing yang tersamar melalui layanan pemendek URL t.co.\r\nPenelitian ini bertujuan untuk membangun sistem deteksi otomatis menggunakan\r\nmetode Machine Learning guna mengatasi keterbatasan sistem moderasi\r\nkonvensional. Dataset yang digunakan berjumlah 1.000 tautan yang dikumpulkan\r\nmelalui teknik crawling pada platform X, dengan komposisi seimbang antara\r\nkategori aman dan berbahaya. Fitur leksikal dan statistik, khususnya Shannon\r\nEntropy, diekstraksi dari struktur URL untuk melatih model klasifikasi berbasis\r\nalgoritma XGBoost. Hasil penelitian menunjukkan bahwa fitur entropi memiliki\r\npengaruh paling signifikan dalam mendeteksi anomali pada tautan pendek.\r\nPerforma model dievaluasi menggunakan Confusion Matrix, menghasilkan tingkat\r\nakurasi dan stabilitas metrik yang optimal dalam mengidentifikasi ancaman siber\r\npada ekosistem platform X. Sistem deteksi ini kemudian diimplementasikan ke\r\ndalam aplikasi purwarupa bernama \"Unmask\" sebagai solusi perlindungan\r\npengguna secara real-time.",
      "ispublished": "pub",
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      "title": "DETEKSI MALICIOUS LINK PADA KONTEN DEWASA DI\r\nPLATFORM X MENGGUNAKAN METODE \r\nMACHINE LEARNING",
      "rev_number": 15,
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            "family": "Ashari",
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            "given": "Muhammad Iqbal"
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      "date": "2026-02-03",
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      "department": "Fakultas Ilmu Komputer",
      "metadata_visibility": "show",
      "keywords": "Phishing, Machine Learning, XGBoost, Platform X, Konten \r\nDewasa, Adult Content."
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