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Asep Syaputra
Sekolah Tinggi Teknologi Pagaralam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Implementation of Machine Learning Algorithms for Predicting Student Final GPA Using Multiclass Classification Models Sayaputra, Asep
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7015

Abstract

This study aims to predict students’ final Grade Point Average (GPA) and study duration by applying the Random Forest Regressor algorithm based on historical academic data. The dataset includes key variables such as semester GPA, socio-economic background, demographic information, study habits, and the complexity level of the enrolled courses. The regression analysis results indicate that the model's performance was suboptimal, with a Mean Squared Error (MSE) of 0.341 for GPA prediction and 3.831 for study duration estimation. Additionally, the negative R-squared (R²) values reflect the model's limited ability to explain data variability. As an alternative, a multi-class classification approach was implemented to categorize students into final GPA groups, including Cum Laude, Very Satisfactory, Satisfactory, and Adequate. At this stage, the model achieved a remarkably high accuracy of 99.8% with an error rate of only 0.03. These findings demonstrate that the classification approach is more effective than regression in predicting academic performance. This research contributes to the development of data-driven academic decision support systems. Future studies are recommended to explore feature optimization techniques and alternative algorithms to enhance overall prediction performance.
Co-Authors Asep Deden Rahmat S Barrorotul Azizah Barrorotul Azizah Buhori Muslim Buhori Muslim Dedi Setiadi Dhiniati, Fameira Didi Ardiansyah Edowinsyah Edowinsyah Edowinsyah Edownsyah Ego Winanda Eva Susilawati Eva Susilawati Febriansyah . Ferry Putrawansyah Jang Cik, Idi Nanda S. Prawira Rony, Zahara Tussoleha Sasmita Sasmita Tri Susanti Vike Itteridi Vike Itteridi Wenny Djuarni Yogi Isro Mukti, Yogi Isro