Minimizing Subjectivity in Esports Adjudication: A Decision Support System for Indonesia Sim Racing League Using C4.5 Algorithm

Mu'ammar Dafa'; Delima Sitanggang; Mardi Turnip

doi:10.47709/cnahpc.v8i1.7376

Authors

Mu'ammar Dafa' Faculty of Science and Technology, Universitas Prima Indonesia Medan
Delima Sitanggang Faculty of Science and Technology, Universitas Prima Indonesia Medan
Mardi Turnip Faculty of Science and Technology, Universitas Prima Indonesia Medan

DOI:

https://doi.org/10.47709/cnahpc.v8i1.7376

Keywords:

Decision Support System, Decision Tree C4.5, F1 Racing Incident, Indonesia Sim Racing League.

Abstract

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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References

Barito, E. E., Beng, J. T., & Arisandi, D. (2021). Penerapan algoritma C4.5 untuk klasifikasi mahasiswa penerima bantuan sosial COVID-19. Jurnal Ilmu Komputer dan Sistem Informasi, 10(1). Universitas Tarumanagara.

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Chitra, R., & Seenivasagam, V. (2018). Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease. Journal of Physics: Conference Series, 983(1), 012062. https://doi.org/10.1088/1742-6596/983/1/012062

Gorunescu, F. (2011). Data mining: Concepts, models and techniques. Berlin: Springer.

Han, J., & Kamber, M. (2001). Data mining concepts and techniques. San Francisco, CA: Morgan Kaufmann.

Hssina, B., Merbouha, A., Ezzikouri, H., & Erritali, M. (2014). A comparative study of decision tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, 5(2), 13-19.

Kusrini. (2007). Konsep dan aplikasi sistem pendukung keputusan. Yogyakarta: Andi Offset.

Kustiyahningsih, Y., & Rahmanita, E. (2016). Aplikasi sistem pendukung keputusan menggunakan algoritma C4.5 untuk penjurusan SMA. Simantec, 5(2), 101-108.

Larose, D. T. (2005). Discovering knowledge in data: An introduction to data mining. Hoboken, NJ: John Wiley & Sons.

Luthfiyah, S. I., & Santi, R. C. N. (2022). Sistem pendukung keputusan (SPK) penentuan algoritma dan metode penelitian dengan metode Simple Additive Weighting (SAW). Jurnal Teknologi Informasi dan Komunikasi, 13(2).

Prasetyo, E. (2012). Data mining: Konsep dan aplikasi menggunakan MATLAB. Yogyakarta: Andi.

Quinlan, J. R. (2014). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann. (Original work published 1993).

Rokach, L., & Maimon, O. (2008). Data mining with decision trees: Theory and applications. Singapore: World Scientific.

Sistem pendukung keputusan: Pengertian, komponen, dan manfaat dalam pengambilan keputusan. (n.d.). Retrieved November, 2021, from https://si.ft.unesa.ac.id/post/sistem-pendukung-keputusan-pengertian-komponen-dan-manfaat-dalam-pengambilan-keputusan

Sistem pendukung keputusan merupakan suatu sistem berbasis komputer yang ditujukan untuk membantu dalam pengambilan keputusan. (n.d.). Retrieved December, 2021, from http://repo.darmajaya.ac.id/2238/3/BAB%20II.pdf

Turban, E., Aronson, J. E., & Liang, T. P. (2005). Decision support systems and intelligent systems (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.

Wang, Y. (2021). Prediction of rockburst risk in coal mines based on a locally weighted C4.5 algorithm. IEEE Access, 9.