<![CDATA[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.]]>
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