Predicting Student Academic Performance Using Learning Activity Data: A Comparative Study of Random Forest and Decision Tree Models
DOI:
https://doi.org/10.30606/rjti.v4i3.4032Keywords:
Academic Performance, Learning Activities, Random Forest, Decision Tree, Machine LearningAbstract
This study compares the effectiveness of the Random Forest and Decision Tree algorithms in predicting students' academic performance based on learning activities. The data used included reading scores, writing scores, math scores, and demographic variables such as gender, race/ethnicity, parental level of education, lunch, and test preparation course. The research was carried out through the stages of data cleaning, training and test data sharing, model training, and evaluation using confusion matrix and accuracy, precision, recall, and F1-score metrics. The results show that Random Forest performs best with 97% accuracy, surpassing Decision Tree which has 94% accuracy. The feature importance analysis  revealed that cognitive ability—especially in the reading score, writing score, and math score features—had the greatest influence on prediction results. These findings confirm that the Random Forest model is more reliable and effective as a prediction tool in the academic decision support system to detect the potential for decline in student achievement early.
Downloads
References
N. Sharma, S. Appukutti, U. Garg, J. Mukherjee, and S. Mishra, “Analysis of Student’s Academic Performance based on their Time Spent on Extra-Curricular Activities using Machine Learning Techniques,†International Journal of Modern Education and Computer Science, vol. 15, no. 1, 2023, doi: 10.5815/ijmecs.2023.01.04.
M. Furqon, P. Sinaga, L. Liliasari, and L. S. Riza, “The Impact of Learning Management System (LMS) Usage on Students,†TEM Journal, vol. 12, no. 2, pp. 1082–1089, May 2023, doi: 10.18421/TEM122-54.
M. P. R. I. R. Silva, R. A. H. M. Rupasingha, and B. T. G. S. Kumara, “A Comparative Study of Predicting Students’ Academic Performance Using Classification Algorithms,†in ICARC 2022 - 2nd International Conference on Advanced Research in Computing: Towards a Digitally Empowered Society, 2022. doi: 10.1109/ICARC54489.2022.9753729.
R. Andriani Saputri and L. Rosnita, “A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools,†2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
M. Ghofar Rohman, Z. Abdullah, and S. Kasim, “INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Hybrid Logistic Regression Random Forest on Predicting Student Performance.†[Online]. Available: www.joiv.org/index.php/joiv
B. Deddy Setiawan and D. Wibisono, “PREDICTING STUDENT PERFORMANCE USING MACHINE LEARNING FOR STUDENT MANAGEMENT IN UNIVERSITY,†Jurnal Ilmiah Indonesia, vol. 7, no. 10, 2022, doi: 10.36418/syntax.
M. A. Gotardo, “Using Decision Tree Algorithm to Predict Student Performance,†Indian J Sci Technol, vol. 12, no. 8, pp. 1–8, Feb. 2019, doi: 10.17485/ijst/2019/v12i5/140987.
M. Hasan et al., “Predicting student performance and identifying learning behaviors using decision trees and K-means clustering,†International Journal of Evaluation and Research in Education, vol. 14, no. 5, pp. 3872–3881, Oct. 2025, doi: 10.11591/ijere.v14i5.33815.
N. Sulistiyaningsih and R. Rismayati, “Comparison of Random Forest, Decision Tree, and XGBoost Models in Predicting Student Academic Success,†Journal of Artificial Intelligence and Software Engineering, vol. 5, no. 3, pp. 920–930, 2025, doi: 10.30811/jaise.v5i3.7138.
K. Kesgin, S. Kiraz, S. Kosunalp, and B. Stoycheva, “Beyond Performance: Explaining and Ensuring Fairness in Student Academic Performance Prediction with Machine Learning,†Applied Sciences (Switzerland), vol. 15, no. 15, Aug. 2025, doi: 10.3390/app15158409.
A. I. Gufroni, P. Purwanto, and F. Farikhin, “INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Academic Performance Prediction Using Supervised Learning Algorithms in University Admission.†[Online]. Available: www.joiv.org/index.php/joiv
W. Chong-Wen, L. Sha-Sha, and E. Xu, “Predictors of rapid eye movement sleep behavior disorder in patients with Parkinson’s disease based on Random Forest and decision tree,†PLoS One, vol. 17, no. 6 6, 2022, doi: 10.1371/journal.pone.0269392.
X. Wang, L. Zhang, and T. He, “Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning,†Sustainability (Switzerland), vol. 14, no. 13, 2022, doi: 10.3390/su14137654.
M. S. Ibrahim Alsumaidaie, K. M. Ali Alheeti, and A. K. Alaloosy, “An Assessment of Ensemble Voting Approaches, Random Forest, and Decision Tree Techniques in Detecting Distributed Denial of Service (DDoS) Attacks,†Iraqi Journal for Electrical and Electronic Engineering, vol. 20, no. 1, 2024, doi: 10.37917/ijeee.20.1.2.
M. Schonlau and R. Y. Zou, “The Random Forest algorithm for statistical learning,†Stata Journal, vol. 20, no. 1, 2020, doi: 10.1177/1536867X20909688.
C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,†2021. doi: 10.3389/fenrg.2021.652801.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rahmat Hidayat, Herwis Gultom, Yuda Samudra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Riau Jurnal Teknik Informatika provides open access to anyone so that the information and findings in these articles are useful for everyone. This journal's article content can be accessed and downloaded for free, free of charge, following the creative commons license used.
Riau Jurnal Teknik Informatika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
