Comparative Analysis ofHybrid ModelPerformanceUsing Stacking and Blending Techniques for Student Drop-OutPrediction in MOOC

Comparative Analysis ofHybrid ModelPerformanceUsing Stacking and Blending Techniques for Student Drop-OutPrediction in MOOC

machine learning, classification, stacking, blending, 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 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-modeluses XGBoost. These algorithms are used to build models with stacking and blending techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1-Score. Meanwhile, blending obtained 83.39% accuracy, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validationand confusion matrix techniques which show the same results. That is, blending is 0.86% higher than stacking so it can be concluded that blending has better performance than stacking in the MOOC student dropout prediction case study

Muhammad Ricky Perdana Putra1, Ema Utami

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5760/934

Magister of Informatics Engineering, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia

01-06-2024

FAJAR BAGUS W

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