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      "date": "2026-02-02",
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            "given": "Muhamad Aidhil Fitrah",
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            "family": "Ramadhan"
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      "keywords": "TF-IDF, Berita Hoax, Kernel RBF, Implementasi, Implementation",
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      "department": "Fakultas Ilmu Komputer",
      "title": "IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM)\r\nPADA BERITA HOAX DAN FAKTUAL \r\nBERBASIS TF-IDF",
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      "abstract": "Penyebaran berita bohong atau hoaks melalui judul berita yang provokatif\r\ntelah menjadi tantangan serius di Indonesia. Penelitian ini bertujuan untuk\r\nmengimplementasikan algoritma Support Vector Machine (SVM) untuk\r\nmengklasifikasikan judul berita ke dalam kategori Hoax dan Faktual menggunakan\r\nmetode pembobotan Term Frequency-Inverse Document Frequency (TF-IDF).\r\nDataset yang digunakan berjumlah 2.000 dokumen yang diproses melalui tahapan\r\ntext preprocessing meliputi case folding, punctuation & number removal, dan\r\nstopword removal. Penelitian ini membandingkan kinerja tiga fungsi kernel, yaitu\r\nLinear, Radial Basis Function (RBF), dan Polynomial. Hasil penelitian\r\nmenunjukkan bahwa kernel RBF memberikan performa terbaik dengan akurasi\r\nsebesar 89,50% dan nilai AUC sebesar 0,96. Model tersebut kemudian\r\ndiimplementasikan ke dalam aplikasi berbasis web menggunakan React.js untuk\r\nmendeteksi hoaks secara real-time. Pengujian validitas pada sistem menunjukkan\r\ntingkat keberhasilan sebesar 90%, membuktikan bahwa implementasi SVM\r\nberbasis TF-IDF sangat efektif dalam melakukan verifikasi validitas informasi pada\r\nteks berita.",
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            "given": "Norhikmah",
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