Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC
Despite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses (MOOC) has problems, one of which is the dropout rate (DO) of students, which reaches 93%. As one of the solutions to this problem, machine learning can be utilized...
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Ikatan Ahli Informatika Indonesia
2024-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5760 |
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| _version_ | 1841544043825201152 |
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| author | Muhammad Ricky Perdana Putra Ema Utami |
| author_facet | Muhammad Ricky Perdana Putra Ema Utami |
| author_sort | Muhammad Ricky Perdana Putra |
| collection | DOAJ |
| description | Despite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses (MOOC) has problems, one of which is the dropout rate (DO) of students, which reaches 93%. As one of the solutions to this problem, machine learning can be utilized as a risk management and early warning system for students who have the potential to drop out. The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. As a form of contribution, this study will compare the performance of models built from stacking and blending techniques. The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. These algorithms are used to build models with stacking and mixing techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1 score. Meanwhile, the blend obtained 83.39% precision, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validation and confusion matrix techniques, which show the same results. That is, blending is 0.86% higher than stacking, so it can be concluded that blending performs better than stacking in the MOOC student dropout prediction case study. |
| format | Article |
| id | doaj-art-75b8a6d19cac4104bf8a9e50f4c663f5 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-75b8a6d19cac4104bf8a9e50f4c663f52025-01-13T03:33:46ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-06-018334635410.29207/resti.v8i3.57605760Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOCMuhammad Ricky Perdana Putra0Ema Utami1Universitas Amikom YogyakartaUniversitas Amikom YogyakartaDespite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses (MOOC) has problems, one of which is the dropout rate (DO) of students, which reaches 93%. As one of the solutions to this problem, machine learning can be utilized as a risk management and early warning system for students who have the potential to drop out. The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. As a form of contribution, this study will compare the performance of models built from stacking and blending techniques. The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. These algorithms are used to build models with stacking and mixing techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1 score. Meanwhile, the blend obtained 83.39% precision, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validation and confusion matrix techniques, which show the same results. That is, blending is 0.86% higher than stacking, so it can be concluded that blending performs better than stacking in the MOOC student dropout prediction case study.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5760machine learningclassificationstackingblendingmooc |
| spellingShingle | Muhammad Ricky Perdana Putra Ema Utami Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) machine learning classification stacking blending mooc |
| title | Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC |
| title_full | Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC |
| title_fullStr | Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC |
| title_full_unstemmed | Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC |
| title_short | Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC |
| title_sort | comparative analysis of hybrid model performance using stacking and blending techniques for student drop out prediction in mooc |
| topic | machine learning classification stacking blending mooc |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5760 |
| work_keys_str_mv | AT muhammadrickyperdanaputra comparativeanalysisofhybridmodelperformanceusingstackingandblendingtechniquesforstudentdropoutpredictioninmooc AT emautami comparativeanalysisofhybridmodelperformanceusingstackingandblendingtechniquesforstudentdropoutpredictioninmooc |