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      "abstract": "Keamanan jaringan komputer menghadapi tantangan besar akibat\r\nmeningkatnya volume lalu lintas data dan kecanggihan serangan siber. Penggunaan\r\nIntrusion Detection System (IDS) berbasis Machine Learning menjadi solusi vital,\r\nnamun sering terkendala oleh dimensi data yang tinggi (seperti 41 fitur pada dataset\r\nNSL-KDD) yang membebani komputasi, serta sifat black-box dari model AI yang\r\nsulit divalidasi logikanya oleh manusia. Masalah ini menyebabkan ketidakefisienan\r\nsistem dan keraguan administrator terhadap keputusan blokir yang diambil oleh\r\nIDS. Penelitian ini bertujuan untuk mengoptimalkan kinerja IDS sekaligus\r\nmemberikan transparansi keputusan. Metode yang digunakan adalah Hybrid\r\nFeature Selection yang menggabungkan kecepatan Random Forest (Filter) dan\r\nketelitian Recursive Feature Elimination with Cross-Validation (Wrapper) untuk\r\nmenyeleksi fitur terbaik. Selanjutnya, algoritma Decision Tree digunakan untuk\r\nmengekstraksi aturan logika (Rule Extraction) dari setiap serangan. Hasil penelitian\r\nmenunjukkan bahwa sistem mampu mereduksi 41 fitur menjadi 23 fitur optimal\r\ndengan mempertahankan akurasi validasi sebesar 99,73%. Sistem juga berhasil\r\nmenghasilkan \"kamus aturan\" yang transparan untuk 40 jenis serangan berbeda,\r\nsehingga dapat dimanfaatkan oleh praktisi keamanan jaringan untuk memvalidasi\r\ninsiden keamanan secara cepat dan akurat.",
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      "title": "OPTIMASI DAN DETEKSI INTRUSI MENGGUNAKAN \r\nHYBRID FEATURE SELECTION DAN DECISION TREE\r\nRULE-BASED PADA DATA NSLKDD",
      "rev_number": 14,
      "thesis_type": "bachelor",
      "department": "Fakultas Ilmu Komputer",
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      "keywords": "Intrusion Detection System, Hybrid Feature Selection, RFECV, Decision\r\nTree, Explainable AI.",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/31900",
      "date": "2026-01-28",
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            "family": "Putra",
            "given": "Riki Nur Indra",
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