Analisis Komparasi Algoritma Random Forest dan Support Vector Machine untuk Deteksi Intrusi Jaringan
DOI:
https://doi.org/10.29103/techsi.v16i2.25811Abstract
Meningkatnya kompleksitas serangan siber menuntut adanya sistem keamanan jaringan yang adaptif dan efisien. Intrusion Detection System (IDS) tradisional seringkali memiliki keterbatasan dalam mengenali pola serangan baru. Penelitian ini bertujuan untuk mengevaluasi kinerja dua algoritma Machine Learning, yaitu Random Forest (RF) dan Support Vector Machine (SVM), dalam mengklasifikasikan trafik jaringan normal dan serangan. Eksperimen dilakukan menggunakan dataset NSL-KDD dengan melibatkan seluruh 41 fitur melalui tahapan preprocessing, normalisasi, dan validasi data dengan rasio 80:20. Hasil pengujian menunjukkan bahwa algoritma Random Forest mengungguli SVM dengan tingkat akurasi mencapai 99.78%, presisi 1.00, dan recall 1.00. Sebaliknya, SVM mencatatkan akurasi sebesar 99.03%. Selain unggul dalam akurasi, Random Forest terbukti lebih efisien dengan waktu pelatihan (training time) rata-rata 3.72 detik, hampir dua kali lebih cepat dibandingkan SVM yang membutuhkan 6.61 detik. Berdasarkan hasil tersebut, Random Forest direkomendasikan sebagai algoritma yang lebih efektif untuk implementasi IDS pada lingkungan yang membutuhkan respons waktu nyata (real-time).
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Copyright (c) 2026 Hasanal Fachri Satia Simbolon, Ade Linhar P, Rafi Septiawan Putra, Fahmi Izhari
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