Academic Performance Prediction of PTIK Students through Machine Learning Models at Universitas Negeri Medan

Tansa Trisna Astono Putri, Reni Rahmadani, Rosma Siregar, Hanapi Hasan

Abstract


This study addressed the need for an effective approach to predicting student academic performance in higher education using data-driven methods. The study aimed to implement machine learning models to predict the academic performance of students in the Information and Communication Technology Education Study Program at Universitas Negeri Medan. A quantitative predictive design was employed using a dataset of 40 student records. Five classification models were tested, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The results showed that all models produced strong predictive performance. Decision Tree achieved the highest accuracy at 93.1%, Logistic Regression produced the highest precision at 95.9% and the highest F1-score at 93.2%, while Support Vector Machine obtained the highest recall at 93.2%. These findings indicated that machine learning was feasible for predicting student academic performance in the study program. The study concluded that Logistic Regression provided the most balanced overall performance and had strong potential to support early academic intervention and data-based academic decision making in higher education.

Keywords


academic performance prediction; machine learning; higher education; student classification; educational data mining; early academic intervention.

Full Text:

PDF

References


Bin Roslan, M. H., & Chen, C. J. (2022). Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015–2021). International Journal of Emerging Technologies in Learning (IJET), 17(05), 147–179. https://doi.org/10.3991/ijet.v17i05.27685

Ersozlu, Z., Taheri, S., & Koch, I. (2024). A Review of Machine Learning Methods Used for Educational Data. Education and Information Technologies, 29, 22125–22145. https://doi.org/10.1007/s10639-024-12704-0

Francis, B. K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6), 162. https://doi.org/10.1007/s10916-019-1295-4

Hasan, H., Ambiyar, Wulansari, R. E., Maksum, H., & Putri, T. T. A. (2024). Investigating the Impacts of A Simulation-Based Learning Model Using Simulation Virtual Laboratory on Engineering Students. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 8(3), 1569–1576. https://doi.org/10.33395/sinkron.v8i3.13747

Hasan, H., Yulastri, A., Ganefri, Putri, T. T. A., & Marta, R. (2024). Prediction of Student Entrepreneurship Future Work Based on Entrepreneurship Course Using the Naïve Bayes Classifier Model. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 8(1), 525–532. https://doi.org/10.33395/sinkron.v9i1.13293

Issah, I., Appiah, O., Appiahene, P., & Inusah, F. (2023). A Systematic Review of the Literature on Machine Learning Application of Determining the Attributes Influencing Academic Performance. Decision Analytics Journal, 7, 100204. https://doi.org/10.1016/j.dajour.2023.100204

Khan, A., & Ghosh, S. K. (2021). Student Performance Analysis and Prediction in Classroom Learning: A Review of Educational Data Mining Studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3

Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Educational Technology & Society, 21(2), 220–232. https://eric.ed.gov/?id=EJ1175301

Putri, T. T. A., Rahmadani, R., & Hasan, H. (2024). Anxiety in Programming Course of University Students: Does It Affect Students’ Performance? Instal: Jurnal Komputer, 16(03), 445–452. https://doi.org/10.54209/jurnalinstall.v16i03.284

Putri, T. T. A., Yahaya, W. A. J. W., Mokmin, N. A. M., & Sriadhi. (2025). Examining the Effect of Machine-Learning Programming Simulator on Student Performance and Student Anxiety. International Journal of Information and Education Technology, 15(7), 1530–1538. https://doi.org/10.18178/ijiet.2025.15.7.2354

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157

Yağc, M. (2022). Educational Data Mining: Prediction of Students’ Academic Performance Using Machine Learning Algorithms. Smart Learning Environments, 9, 11. https://doi.org/10.1186/s40561-022-00192-z




DOI: https://doi.org/10.30596/jcositte.v7i1.29570

Refbacks

  • There are currently no refbacks.