Comparison Of the Accuracy of Decision Tree Algorithms C4.5 And C5.0 In Predicting Tuition Payment Delays at Mts. Al-Jabar Bali
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Delays in the payment of Educational Development Contributions (SPP) have become a major issue impacting financial management at MTs. Al-Jabar Bali, with approximately 60% of students experiencing payment delays each year. This study aims to compare the accuracy of Decision Tree algorithms C4.5 and C5.0 in predicting SPP payment delays. The research method adopts the CRISP-DM approach and is implemented using Python on the Google Colaboratory platform. The data used includes students’ payment histories, parents' occupations, and income. The models were evaluated using Accuracy, Precision, and Recall metrics. The results show that the C5.0 algorithm has higher accuracy (98%) compared to C4.5 (89%). The C5.0 algorithm is recommended as an effective predictive model to assist schools in making strategic financial management decisions.
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Dewi, N. (2025). Comparison Of the Accuracy of Decision Tree Algorithms C4.5 And C5.0 In Predicting Tuition Payment Delays at Mts. Al-Jabar Bali. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 7(3), 40-50. Retrieved from https://infoteks.org/journals/index.php/jsikti/article/view/207
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[18] D. S. Wardhana, A. W., Patimah, E., Shafarindu, A. I., Siahaan, Y. M., Haekal, B. V., & Prasvita, “Klasifikasi Data Penjualan pada Supermarket dengan Metode Decision Tree.,” Senamika, 2(1), 660– 667., 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/13 89
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[21] M. Hakimi, M. S. Zarinkhail, H. Ghafory, and S. A. Hamidi, “Revolutionizing Technology Education with Artificial Intelligence and Machine Learning : A Comprehensive Systematic Literature Review,” vol. 5, no. 2, pp. 94–110, 2024.
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[23] M. Hakimi, Zarinkhail, M.S., Ghafory, H., & Hamidi, S.H. “Revolutionizing Technology Education with Artificial Intelligence and Machine Learning: A Comprehensive Systematic Literature Review,” TIERS Information Technology Journal, 5(7), 94-110., 2024, doi: https://doi.org/10.38043/tiers.v6i2.5640.
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