Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study
Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple...
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| Format: | Article |
| Language: | English |
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Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap
2025-06-01
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| Series: | Journal of Innovation Information Technology and Application |
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| Online Access: | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2540 |
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| author | Md. Wira Putra Dananjaya Putu Gita Pujayanti |
| author_facet | Md. Wira Putra Dananjaya Putu Gita Pujayanti |
| author_sort | Md. Wira Putra Dananjaya |
| collection | DOAJ |
| description | Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Evaluation results on the test data revealed that the Multiple Linear Regression (MLR) model unexpectedly delivered the most superior performance, achieving an R2 value of 0.7324 and the lowest RMSE of 2.0391. Further analysis from non-linear models identified Attendance and Hours_Studied as the two factors with the highest predictive influence. This study concludes that interpretable models like MLR can be highly effective when supported by relevant features, offering practical implications for institutions to develop effective early warning systems by focusing on key, actionable factors. |
| format | Article |
| id | doaj-art-36b6d876ef2e420aa1470ef41c9cb833 |
| institution | Kabale University |
| issn | 2716-0858 2715-9248 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap |
| record_format | Article |
| series | Journal of Innovation Information Technology and Application |
| spelling | doaj-art-36b6d876ef2e420aa1470ef41c9cb8332025-08-20T03:29:44ZengPusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri CilacapJournal of Innovation Information Technology and Application2716-08582715-92482025-06-0171435210.35970/jinita.v7i1.25401652Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative StudyMd. Wira Putra Dananjaya0Putu Gita Pujayanti1Universitas Pendidikan NasionalUniversitas Pendidikan GaneshaAcademic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Evaluation results on the test data revealed that the Multiple Linear Regression (MLR) model unexpectedly delivered the most superior performance, achieving an R2 value of 0.7324 and the lowest RMSE of 2.0391. Further analysis from non-linear models identified Attendance and Hours_Studied as the two factors with the highest predictive influence. This study concludes that interpretable models like MLR can be highly effective when supported by relevant features, offering practical implications for institutions to develop effective early warning systems by focusing on key, actionable factors.https://ejournal.pnc.ac.id/index.php/jinita/article/view/2540machine learningacademic performance predictioncomparative analysis |
| spellingShingle | Md. Wira Putra Dananjaya Putu Gita Pujayanti Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study Journal of Innovation Information Technology and Application machine learning academic performance prediction comparative analysis |
| title | Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study |
| title_full | Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study |
| title_fullStr | Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study |
| title_full_unstemmed | Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study |
| title_short | Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study |
| title_sort | application of machine learning for academic outcome prediction a methodological comparative study |
| topic | machine learning academic performance prediction comparative analysis |
| url | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2540 |
| work_keys_str_mv | AT mdwiraputradananjaya applicationofmachinelearningforacademicoutcomepredictionamethodologicalcomparativestudy AT putugitapujayanti applicationofmachinelearningforacademicoutcomepredictionamethodologicalcomparativestudy |