<![CDATA[The adjudication of racing incidents in the Indonesia Sim Racing League (ISL) currently faces challenges due to inherent subjectivity, inconsistency, and the time-consuming nature of decisions that rely solely on race stewards’ interpretations. This study develops a Decision Support System (DSS) for penalty recommendation in ISL racing incidents by applying the Decision Tree C4.5 algorithm. Historical incident data were collected directly from Indonesia Sim Racing League Seasons 1 to 3, and an additional synthetic dataset was generated based on predefined incident attributes to support model training. All data were processed using Python in the Google Colab environment to train and evaluate the C4.5 model. Experimental results show that the proposed DSS achieved an overall accuracy of 90%, indicating strong predictive capability in recommending appropriate penalties under the given dataset configuration. Further evaluation using class-sensitive metrics yielded a macro-average precision of 0.71, a recall of 0.73, and an F1-score of 0.72, reflecting a more balanced performance across penalty classes despite the presence of class imbalance in racing incident data. These results indicate that the model is able to capture relevant decision patterns while maintaining robustness across both majority and minority penalty classes. Overall, this study demonstrates that the proposed DSS can assist race stewards at an early stage of decision-making by narrowing the decision space and reducing subjective bias, thereby supporting fairer and more consistent adjudication processes. The main contribution of this paper lies in presenting one of the first empirical implementations of a DSS for esports racing adjudication using an interpretable C4.5-based approach, providing a transparent and practical foundation for future research on intelligent decision-support systems in competitive sim racing environments.]]>
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