Comparative Analysis of Random Forest and Support Vector Machine Algorithms for Predicting Student Retention at Ibnu Sina University

dimas pradipto, I Made Artha Agastya, Agus Suryadi

Abstract


Student retention is a critical challenge facing higher education institutions, including Ibnu Sina University (UIS), where a significant proportion of students risk not completing their studies. Purpose: This study develops and compares predictive models using Random Forest (RF) and Support Vector Machine (SVM) algorithms to classify student retention into three categories: Active, At-Risk, and Inactive. Methods: Administrative data from 2,389 students across 6 study programs (2021/2022–2023/2024 cohorts) were used, encompassing 18 predictor variables including academic performance (GPA, failed credits), demographic, and socio-economic factors. Class imbalance was handled using SMOTE, and hyperparameter optimization was performed via Grid Search with 5-Fold Cross Validation. Results: RF outperformed SVM across all metrics, achieving accuracy of 92.24%, weighted F1-Score of 92.38%, and macro F1-Score of 82.67%, compared to SVM's 87.63% and 87.79%. Feature importance identified Total Failed Credits (0.2847) and Cumulative GPA (0.2134) as the strongest predictors. Novelty: Unlike prior studies focusing solely on academic data, this research integrates non-academic variables (leave history, parental income) and explicitly addresses class imbalance via SMOTE in a multi-class Indonesian higher education context, providing a practical Early Warning System (EWS) framework.

Keywords


early warning system; random forest; SMOTE; student retention; support vector machine

Full Text:

PDF

References


D. A. Shafiq, M. Marjani, R. A. A. Habeeb, and D. Asirvatham, "Student Retention using Educational Data Mining and Predictive Analytics: A Systematic Literature Review," IEEE Access, Vol. 10, pp. 72480–72503, 2022, DOI: 10.1109/ACCESS.2022.3188767.

S. Hoca and N. Dimililer, "A Machine Learning Framework for Student Retention Policy Development: A Case Study," Applied Sciences, Vol. 15, No. 6, Art. no. 2989, Mar. 2025, DOI: 10.3390/app15062989.

M. Vaarma and H. Li, "Predicting Student Dropouts with Machine Learning: An Empirical Study in Finnish Higher Education," Technology in Society, Vol. 76, Art. No. 102474, Mar. 2024, DOI: 10.1016/j.techsoc.2024.102474.

E. Novianto, S. Suhirman, and D. Prasetyo, "Perbandingan Metode Klasifikasi Random Forest dan Support Vector Machine dalam memprediksi Capaian Studi Mahasiswa," JIPI, Vol. 9, No. 4, pp. 1821–1833, Nov. 2024, DOI: 10.29100/jipi.v9i4.5423.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-Sampling Technique," Journal of Artificial Intelligence Research, Vol. 16, pp. 321–357, 2002, DOI: 10.1613/jair.953.

D. Supriyadi, P. Purwanto, and B. Warsito, "Comparison of Random Forest Algorithm, Support Vector Machine and Neural Network for Classification of Student Satisfaction Towards Higher Education Services," in AIP Conference Proceedings, Vol. 2575, No. 1, Nov. 2022, DOI: 10.1063/5.0106201.

C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, Vol. 20, No. 3, pp. 273–297, Sep. 1995, DOI: 10.1007/BF00994018.

V. Realinho, J. Machado, L. Baptista, and M. V. Martins, "Predicting Student Dropout and Academic Success," Data, Vol. 7, No. 11, Art. No. 146, Nov. 2022, DOI: 10.3390/data7110146.

V. A. Lotkowski, S. B. Robbins, and R. J. Noeth, "The Role of Academic and Non-Academic Factors in Improving College Retention," ACT Policy Report, Iowa City: ACT Inc., 2004.

L. Breiman, "Random Forests," Machine Learning, Vol. 45, No. 1, pp. 5–32, Oct. 2001, DOI: 10.1023/A:1010933404324.

H. S. Park and J. Yoo, "Early Dropout Prediction in Online Learning of University using Machine Learning," JOIV: International Journal on Informatics Visualization, Vol. 5, No. 2, pp. 136–140, 2021, DOI: 10.30630/joiv.5.2.458.

M. Yağçı, "Educational Data Mining: Prediction of Students' Academic Performance using Machine Learning Algorithms," Smart Learning Environments, Vol. 9, No. 1, Art. No. 11, Dec. 2022, DOI: 10.1186/s40561-022-00192-z.

W. Villegas-Ch, J. Govea, and S. Revelo-Tapia, "Improving Student Retention in Institutions of Higher Education Through Machine Learning: A Sustainable Approach," Sustainability, Vol. 15, No. 19, Art. No. 14512, Oct. 2023, DOI: 10.3390/su151914512.

A. Villar and C. R. V. de Andrade, "Supervised Machine Learning Algorithms for Predicting Student Dropout and Academic Success: A Comparative Study," Discover Artificial Intelligence, Vol. 4, No. 1, Art. No. 2, Dec. 2024, DOI: 10.1007/s44163-023-00079-z.

B. Holicza and A. Kiss, "Predicting and Comparing Students' Online and Offline Academic Performance using Machine Learning Algorithms," Behavioral Sciences, Vol. 13, No. 4, Art. no. 289, Apr. 2023, DOI: 10.3390/bs13040289.

E. Ahmed, "Student Performance Prediction using Machine Learning Algorithms," Applied Computational Intelligence and Soft Computing, Vol. 2024, Art. No. 4067721, 2024, DOI: 10.1155/2024/4067721.

R. D. Deleña et al., "Predicting Student Retention: A Comparative Study of Machine Learning Approach Utilizing Sociodemographic and Academic Factors," Systems and Soft Computing, Vol. 7, Art. No. 200352, Dec. 2025, DOI: 10.1016/j.sasc.2025.200352.

T. Wongvorachan, S. He, and O. Bulut, "A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining," Information, Vol. 14, No. 1, Art. No. 54, Jan. 2023, DOI: 10.3390/info14010054.

I. Markoulidakis and G. Markoulidakis, "Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis," Technologies, Vol. 12, No. 7, Art. No. 113, Jul. 2024, DOI: 10.3390/technologies12070113.

O. Rainio, J. Teuho, and R. Klén, "Evaluation Metrics and Statistical Tests for Machine Learning," Scientific Reports, Vol. 14, No. 1, Art. No. 6086, Mar. 2024, DOI: 10.1038/s41598-024-56706-x.

K. L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy, "Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions," Mathematics, Vol. 12, No. 24, Art. No. 3935, Dec. 2024, DOI: 10.3390/math12243935.




DOI: https://doi.org/10.32520/stmsi.v15i4.6298

Article Metrics

Abstract view : 4 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.