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Submitted
January 22, 2025
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July 3, 2025
Published
July 6, 2025
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Abstract

Deteksi anomali akibat serangan flood merupakan tantangan utama dalam pengelolaan keamanan jaringan modern. Penelitian ini mengusulkan penerapan algoritma K-Nearest Neighbors (KNN) dalam kerangka supervised learning untuk membangun model Network Flood Detection (NFD) yang dievaluasi menggunakan metrik performa yang lebih komprehensif, yaitu akurasi, presisi, dan recall. Model dikembangkan berdasarkan fitur jaringan seperti bandwidth masuk, bandwidth keluar, ping, serta distribusi trafik flood dan normal. Data diperoleh dari laporan jaringan instansi secara real-time dan historis, yang kemudian diproses melalui tahapan normalisasi, pengurangan fitur, dan penghapusan noise. Hasil evaluasi menunjukkan bahwa model mampu mencapai akurasi hingga 92,42% dengan skor F1 yang seimbang antar kelas. Selain itu, kurva ROC dengan AUC sebesar 0,99 menunjukkan bahwa model memiliki kemampuan diskriminasi yang tinggi dalam membedakan trafik flood dan normal. Temuan ini menunjukkan bahwa KNN, meskipun sederhana, dapat digunakan secara efektif dalam sistem deteksi serangan flood jika didukung oleh data yang representatif dan proses evaluasi yang tepat.

Keywords

Keamanan Jaringan Supervised Learning KNN Network Flood Deteksi Anomali

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Copyright (c) 2025 roni habibi, Naufal Dekha Widana

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habibi, roni, & Widana, N. D. (2025). Penerapan Algoritma K-Nearest Neighbors untuk Deteksi Serangan Network Flood Berbasis Supervised Learning. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 4(2), 129–137. https://doi.org/10.20885/snati.v4.i2.38599
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