Analysis and Prediction of Student Academic Performance Using the Random Forest Algorithm
Abstract
Student academic performance is one of the important indicators of success in the learning process. Therefore, a method is needed that can analyze and accurately predict students' academic performance. This study aims to analyze and predict students' academic performance using the Random Forest algorithm. The dataset used consists of 649 student records with 33 attributes covering student characteristics, family background, social activities, and academic grades. The students’ final grade (G3) is used as the target variable in the classification process.The research stages include data exploration, data preprocessing, splitting the data into training and testing sets, building the Random Forest model, and evaluating the model. The results show that the Random Forest model is able to achieve an accuracy rate of 92.31%. Testing using a confusion matrix indicates that the model has a relatively low prediction error rate. In addition, testing using the cross-validation method produces an average accuracy of 91.21%, indicating that the model has good stability. Feature importance analysis shows that previous academic grades, namely G2 and G1, are the most influential factors affecting students' academic performance. The results of this study indicate that the Random Forest algorithm can be effectively used to predict students' academic performance and can assist educational institutions in data-driven decision-making.
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References
L. H. Alamri, R. S. Almuslim, M. S. Alotibi, D. K. Alkadi, I. U. Khan, and N. Aslam, “Predicting Student Academic Performance Using Support Vector Machine and Random Forest,” ACM International Conference Proceedings Series, vol. PartF168981, pp. 100–107, 2020.
M. Nachouki and M. A. Naaj, “Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm,” International Journal of Distance Education Technologies, vol. 20, no. 1, pp. 1–17, 2022.
C. Beaulac and J. S. Rosenthal, “Predicting University Students’ Academic Success and Major Using Random Forests,” Research in Higher Education, vol. 60, no. 7, pp. 1048–1064, 2019.
J. Fathurahman, D. Hartanti, and S. Sopingi, “Sentiment Analysis of Joglo Wifi UMKM Service Using the Naive Bayes Method,” Jurnal Inotera, vol. 9, no. 2, pp. 370–377, 2024.
Y. Abubakar, N. Bahiah, and H. Ahmad, “Prediction of Students’ Performance in an E-Learning Environment Using Random Forest,” International Journal of Innovative Computing, vol. 7, no. 2, pp. 1–5, 2017.
R. Amin and A. S. F. Utami, “Prediction of Exam Scores Based on Student Habits Using the Random Forest Regressor Algorithm,” Information Systems Education Professionals Journal, vol. 10, no. 2, p. 149, 2025.
M. H. Sukri and Y. Handrianto, “Application of the C4.5 Algorithm in Predicting Student Achievement at SMPN 51 Jakarta,” Informatics and Computer Engineering Journal, vol. 4, no. 1, pp. 11–24, 2024.
R. A. Saputri, A. Asrianda, and L. Rosnita, “A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools,” Journal of Applied Informatics and Computing, vol. 9, no. 3, pp. 1042–1049, 2025.
R. Z. Arifin, H. Firmansyah, and W. Asriyani, “Prediction of Student Graduation Based on Demographic and Academic Data Using the Student Performance Dataset,” Journal of Community Service and Educational Research, vol. 4, no. 2, pp. 13300–13307, 2025.
A. Fatunnisa and H. Marcos, “Prediction of On-Time Graduation for Computer Engineering Vocational School Students Using the Random Forest Algorithm,” Journal of Informatics Management, vol. 14, no. 4, pp. 101–111, 2024.
Jupron and Sutrisno, “Analysis of Heart Disease Using the Random Forest Method,” Jurnal Inotera, vol. 10, no. 1, pp. 167–174, 2025.
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