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All Journal MODELING: Jurnal Program Studi PGMI International Journal of Islamic Business and Economics (IJIBEC) Journal of Islamic Monetary Economics and Finance Mobile and Forensics AL-AF'IDAH: Jurnal Pendidikan Bahasa Arab dan Pengajarannya Journal of Community Service and Society Empowerment Al-Mau'izhoh: Jurnal Pendidikan Agama Islam Al-Irfan: Journal Of Arabic Literature and Islamic Studies
Ratna Komalasari, Ratna
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Social Sciences Other

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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 Achmad Firdaus Agung Purwo Wicaksono Al-Kaosari, Muhammad Angin, Pettia Weharima Perangin Cahyani , Ardia Pramesti Regita Cahyani, Ardia Pramesti Regita Dewi, Erwita Triana Faisal Hakim, Muhammad Firmansyah, Ridwan Fitriani, Maulida Ayu Kaosari, Muhammad Al- Khairo, Nida Zahratul Komala Sari, Lulu Latipah, Siti Latipah Margani, Ahmad Masithah, Siti Nur Maujud, Fathul Maulidi, Liazi Maulidina, Ana Mukhlis Prasetyo Aji Murniati Mukhlisin, Murniati Santi Rimadias Siti Asiah Sukma Ria Qurrota Ayun, Zuzun Suparmanto Wiwik Handayani