Deteksi Anomali Serangan Siber Pada Log Server SIAKAD STKIP DDI Pinrang Menggunakan Algoritma Random Forest Berbasis Analisis Real-Time

Ismail Sulaeman; Hazriani Hazriani; Yuyun Yuyun

doi:10.55606/jcsr-politama.v4i3.6229

Authors

Ismail Sulaeman Universitas Handayani Makassar
Hazriani Hazriani Universitas Handayani Makassar
Yuyun Yuyun Universitas Handayani Makassar

DOI:

https://doi.org/10.55606/jcsr-politama.v4i3.6229

Keywords:

Anomaly Detection, Cyber Security, Machine Learning, Random Forest, Server Logs

Abstract

This study aims to develop a machine learning-based cyber attack anomaly detection system on the Academic Information System (SIAKAD) server logs of STKIP DDI Pinrang using the Random Forest algorithm. The research employed a quantitative experimental approach using server log data from July 2022 to December 2024, complemented by brute force and HTTP flood attack simulations conducted from January to March 2025. The research stages included log preprocessing, behavioral feature extraction, data labeling, model training, and system performance evaluation. The main features used were request rate, error ratio, path diversity, and access frequency to the sensitive /lupapassword endpoint. The results showed that the Random Forest algorithm achieved an accuracy of 98.3%, precision of 97.5%, recall of 96.8%, and F1-Score of 97.1% with a False Positive Rate of 1.2%. In addition, the system demonstrated an average inference time of 28 ms, enabling near real-time detection without affecting server performance. This study proves that Random Forest is effective as a cyber attack anomaly detection solution for academic environments with limited technological infrastructure.

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References

Abarnaa, M., Anandhi, S., Anaswara, J. S., & Shanthini, J. (2025). Enhancing cloud container security using random forest and AdaBoost algorithm. AIP Conference Proceedings, 3204(1), 050006. https://doi.org/10.1063/5.0248609

Andriana, I., & Setiawan, I. (2024). Optimized feature selection for DDoS attack detection using decision tree and chi-square. Journal of Applied Informatics and Computing, 8(1). https://ijream.org/papers/IJREAMSSJ2212

Aulia, T., Nafila, N., Triando, F., & Hilabi, F. (2025). Perancangan dan implementasi sistem informasi peminjaman buku berbasis web pada perpustakaan pribadi menggunakan framework Laravel. Journal of Science, Technology, and Innovation, 1(2), 319–331. https://doi.org/10.65310/0wfaf157

Cahyaningtyas, S., Fudholi, D. H., & Hidayatullah, A. F. (2021). Deep learning for aspect-based sentiment analysis on Indonesian hotels reviews. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1300

Dachi, J. M. A. S., & Sitompul, P. (2023). Analisis perbandingan algoritma XGBoost dan algoritma random forest ensemble learning pada klasifikasi keputusan kredit. Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam, 2(2), 87–103. https://doi.org/10.55606/jurrimipa.v2i2.1470

Fadhlurrohman, M., Muliawati, A., & Hananto, B. (2021). Analisis kinerja intrusion detection system pada deteksi anomali dengan metode decision tree terhadap serangan siber. Jurnal Ilmu Komputer, 8(2), 90–94. https://doi.org/10.29244/jika.8.2.90-94

Fitri, M. A., Utami, F. N., Rahma, A. H., & Putranta, H. (2025). Gloria game board: Inovasi media pembelajaran sejarah peradaban Islam dengan desain bergaya monopoli. Journal of Science, Technology, and Innovation, 1(1), 11–20.

Jiang, X., Jia, R., & Zhang, F. (2025). Deep learning-based user behavior anomaly detection and threat early warning in cloud computing environments. International Journal of Advanced Computing, 4(3), Article 112. https://academianexusjournal.com/index.php/anj/article/view/45

Kurniawan, D., & Sari, N. (2025). Framework hybrid ML untuk mitigasi serangan pada infrastruktur pemerintahan. Jurnal Keamanan Siber dan Forensik Digital. https://www.jespublication.com/uploads/2025-V16I50146

Rahmadaniar, I., Tondang, D. A. A., Fernando, B. S., & Setiawan, A. (2024). Implementasi firewall menggunakan iptables untuk melindungi server dari serangan DDoS. Journal of Internet and Software Engineering, 1(3), 10. https://doi.org/10.47134/pjise.v1i3.2564

Silaen, K. E., Soewito, B., Anggreainy, M. S., & Kurniawan, A. (2024). ApiPot: A novelty API honeypot for exhaustive attack feature detection in HTTP protocol. IEEE Access, 12, 12345–12356. https://doi.org/10.1109/FiCloud62933.2024.00050

Tan, H., & Liu, X. (2025). The effectiveness of hybrid machine learning in mitigating zero-day DDoS attacks. Scientific Reports, 15. https://www.nature.com/articles/s41598-025-10092-0

Yaddarabullah, Y., Wahyudin, M. F. A., Lestari, D., Pranoto, G. T., Opitasari, O., & Bajsair, F. (2025). Distributed denial of service attack detection model with random forest. In 2025 International Conference on Information and Communications Technology (ICOIACT). IEEE. https://doi.org/10.1109/ICoCSETI63724.2025.11019192

Yasmin, Y., Fadhilah, H., Fikri, M., & Huda, M. (2025). Analisis elemen gamifikasi dan micro-interactions pada aplikasi Duolingo: Tinjauan literatur sistematis. Journal of Science, Technology, and Innovation, 1(2), 278–287. https://doi.org/10.65310/tmer5v81

Zikry, A., & Siregar, F. A. (2026). Performance analysis of logistic regression and SVM (support vector machine) algorithms on e-football mobile game review sentiment. Journal of Science, Technology, and Innovation, 1(3), 212–224. https://doi.org/10.65310/073zdt91