Analisis Perbandingan Metode Seleksi Fitur pada Model Klasifikasi Decission Tree untuk Deteksi Serangan di Jaringan Komputer
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Published:
May 31, 2024
Keywords:
Decission tree, Intrusion detection system, Keamanan jaringan, Infrastruktur jaringan
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Abstract
Perkembangan informasi dan teknologi memerlukan teknik pengamanan yang tepat. Potensi terjadinya kebocoran data dan informasi di era digital sangat tinggi apabila tidak ditangani dengan serius. Beberapa serangan berbahaya yang terjadi adalah spam, Denial of Service Attack, ARP Poisoning, SQL Injection, U2L, R2L dan Probing. Penelitian sebelumnya telah mengenalkan pendekatan deteksi serangan berbahaya seperti menggunakan klasifikasi, klusterisasi dan analisis statistik. Namun analisis penggunaan fitur terbaik perlu dilakukan untuk mendapatkan hasil model klasifikasi yang optimal. Pada penelitian ini, menganalisis dan mencari metode seleksi fitur terbaik yang dapat diimplementasikan pada model klasifikasi berbasis machine learning untuk mendeteksi serangan di jaringan. Dataset yang digunakan adalah UNSW-NB15, dan dilakukan beberapa proses seperti data transformasi, Data normalisasi, seleksi Fitur dan Klasifikasi. Perbandingan teknik seleksi fitur yang digunakan antara lain ANOVA, UNIVARIATE dan ChiSquare. Tujuan penelitian ini adalah untuk meningkatkan akurasi, precision dan recall pada model klasifikasi Decision Tree. Hasil penelitian pengujian menunjukkan bahwa metode seleksi fitur terbaik dalam model klasifikasi adalah metode ANOVA dengan hasil nilai Area Under Curve sebesar 0.989, nilai F1-score adalah 0.999, akurasi deteksi adalah 0.999, nilai precission adalah 0.999 dan recall adalah 0.999. Hasil penelitian ini dapat digunakan untuk menyempurnakan model Intrusi Detection System berbasis machine learning.
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How to Cite
Hariyanti, E., Hostiadi, D. P., Anggreni, Yohanes Priyo Atmojo, I Made Darma Susila, & Tangkawarow, I. (2024). Analisis Perbandingan Metode Seleksi Fitur pada Model Klasifikasi Decission Tree untuk Deteksi Serangan di Jaringan Komputer. Jurnal Sistem Dan Informatika (JSI), 18(2), 208-217. https://doi.org/10.30864/jsi.v18i2.615
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