Student Dropout Prediction Using Random Forest and XGBoost Method
Background: The increasing dropout rate in Indonesia poses significant challenges to the education system, particularly as students advance through higher education levels. Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective:...
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Universitas Nusantara PGRI Kediri
2025-02-01
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| Series: | Intensif: Jurnal Ilmiah Penelitian Teknologi dan Penerapan Sistem Informasi |
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| Online Access: | https://ojs.unpkediri.ac.id/index.php/intensif/article/view/21191 |
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| author | Lalu Ganda Rady Putra Didik Dwi Prasetya Mayadi Mayadi |
| author_facet | Lalu Ganda Rady Putra Didik Dwi Prasetya Mayadi Mayadi |
| author_sort | Lalu Ganda Rady Putra |
| collection | DOAJ |
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Background: The increasing dropout rate in Indonesia poses significant challenges to the education system, particularly as students advance through higher education levels. Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. Methods: A quantitative study was conducted using a dataset of 4,424 instances with 34 attributes, categorized into Dropout, Graduate, and Enrolled. The performance of Random Forest and XGBoost was compared based on accuracy, specificity, and sensitivity. Results: Random Forest achieved the highest accuracy at 80.56%, with a specificity of 76.41% and sensitivity of 72.42%, outperforming XGBoost. While XGBoost was slightly less accurate, it remained a competitive approach for student attrition prediction. Conclusion: The findings highlight Random Forest's robustness in handling extensive datasets with diverse attributes, making it a reliable tool for identifying at-risk students. This study underscores the potential of machine learning in addressing educational challenges. Future research should explore advanced ensemble techniques, such as the Ensemble Voting Classifier, or deep learning models to further enhance prediction accuracy and scalability.
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| format | Article |
| id | doaj-art-5794df456f0b4faba2a3f76b2277d318 |
| institution | DOAJ |
| issn | 2580-409X 2549-6824 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Universitas Nusantara PGRI Kediri |
| record_format | Article |
| series | Intensif: Jurnal Ilmiah Penelitian Teknologi dan Penerapan Sistem Informasi |
| spelling | doaj-art-5794df456f0b4faba2a3f76b2277d3182025-08-20T02:55:09ZengUniversitas Nusantara PGRI KediriIntensif: Jurnal Ilmiah Penelitian Teknologi dan Penerapan Sistem Informasi2580-409X2549-68242025-02-019110.29407/intensif.v9i1.21191Student Dropout Prediction Using Random Forest and XGBoost MethodLalu Ganda Rady Putra0Didik Dwi Prasetya1Mayadi Mayadi2Universitas Negeri MalangUniversitas Negeri MalangUniversiti Teknologi Mara Background: The increasing dropout rate in Indonesia poses significant challenges to the education system, particularly as students advance through higher education levels. Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. Methods: A quantitative study was conducted using a dataset of 4,424 instances with 34 attributes, categorized into Dropout, Graduate, and Enrolled. The performance of Random Forest and XGBoost was compared based on accuracy, specificity, and sensitivity. Results: Random Forest achieved the highest accuracy at 80.56%, with a specificity of 76.41% and sensitivity of 72.42%, outperforming XGBoost. While XGBoost was slightly less accurate, it remained a competitive approach for student attrition prediction. Conclusion: The findings highlight Random Forest's robustness in handling extensive datasets with diverse attributes, making it a reliable tool for identifying at-risk students. This study underscores the potential of machine learning in addressing educational challenges. Future research should explore advanced ensemble techniques, such as the Ensemble Voting Classifier, or deep learning models to further enhance prediction accuracy and scalability. https://ojs.unpkediri.ac.id/index.php/intensif/article/view/21191Student DropoutPredictionRandom ForestXGBoost |
| spellingShingle | Lalu Ganda Rady Putra Didik Dwi Prasetya Mayadi Mayadi Student Dropout Prediction Using Random Forest and XGBoost Method Intensif: Jurnal Ilmiah Penelitian Teknologi dan Penerapan Sistem Informasi Student Dropout Prediction Random Forest XGBoost |
| title | Student Dropout Prediction Using Random Forest and XGBoost Method |
| title_full | Student Dropout Prediction Using Random Forest and XGBoost Method |
| title_fullStr | Student Dropout Prediction Using Random Forest and XGBoost Method |
| title_full_unstemmed | Student Dropout Prediction Using Random Forest and XGBoost Method |
| title_short | Student Dropout Prediction Using Random Forest and XGBoost Method |
| title_sort | student dropout prediction using random forest and xgboost method |
| topic | Student Dropout Prediction Random Forest XGBoost |
| url | https://ojs.unpkediri.ac.id/index.php/intensif/article/view/21191 |
| work_keys_str_mv | AT lalugandaradyputra studentdropoutpredictionusingrandomforestandxgboostmethod AT didikdwiprasetya studentdropoutpredictionusingrandomforestandxgboostmethod AT mayadimayadi studentdropoutpredictionusingrandomforestandxgboostmethod |