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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURIKOM (Jurnal Riset Komputer) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Mobile and Forensics Jurnal Pengabdian Masyarakat Bumi Raflesia Journal of Soft Computing Exploration Melek IT: Information Technology Journal Jurnal Informatika: Jurnal Pengembangan IT Lex Scientia Law Review Prosiding Seminar Nasional CORISINDO
Fitriani, Maulida Ayu
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Performance Analysis of Random Forest Algorithm with Smote for Multi-Class Attack Detection Komalasari, Ratna; Aji, Mukhlis Prasetyo; Wicaksono, Agung Purwo; Fitriani, Maulida Ayu
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.14584

Abstract

The increasing sophistication of cyberattacks necessitates the development of detection systems capable of accurately identifying various threat types. Data imbalance within attack logs presents a substantial challenge that can undermine the effectiveness of detection models. This study introduces a multi-class cyberattack detection model employing the Random Forest algorithm, optimized through the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The innovative aspect of this research lies in integrating Random Forests and SMOTE to improve multi-class classification accuracy on local attack log datasets. This approach remains sparsely explored in academic research. The dataset consists of 3000 cyberattack logs from the Information Systems Bureau of Muhammadiyah University Purwokerto, spanning 10 cyberattack categories. The research process involved data collection, pre- processing, division, model training, and evaluation. Results indicate that the model achieved an average F1-macro score of 76% and a weighted average of 93%, with the " Threat Level Medium " feature identified as the most influential predictor. These findings suggest that the combination of Random Forest and SMOTE effectively enhances multi-class detection performance and presents promising prospects for log-based cybersecurity systems in educational and industrial environments.
Co-Authors Abid Yanuar Badharudin Achmad Fauzan, Achmad Agung Purwo Wicaksono Cahyadri, Rizky Aulia Damarsiwi, Dyah Kartika Dany Candra Febrianto Dimara Kusuma Hakim Ermadi Satriya Wijaya, Ermadi Satriya Feri Wibowo Ghifari, Abu Dzar Al Harjono Harjono Hindayati Mustafidah Hindayati, Mustafidah Indriati Amarini Ismail, Noorfajri Khadija, Mutiara Auliya Laily Nurlina Mahazam Afrad Muhammad Hamka Mukhlis Prasetyo Aji Nugroho, Aswin Mulyo pambudi, elindra ambar Ratna Komalasari, Ratna Ridho Muktiadi Sidqiyah, Elis Dhia Supriyono Supriyono Teguh Marhendi Widyantri, Shafrila