Optimization of Naive Bayes and Decision Tree Algorithms through the Application of Bagging and AdaBoost Techniques for Predicting Student Study Success

Febriyanto, Endi and Wasilah, Wasilah (2025) Optimization of Naive Bayes and Decision Tree Algorithms through the Application of Bagging and AdaBoost Techniques for Predicting Student Study Success. Optimization of Naive Bayes and Decision Tree Algorithms through the Application of Bagging and AdaBoost Techniques for Predicting Student Study Success, 7 (1). pp. 136-149. ISSN ISSN: 2085-3688; e-ISSN: 2460-0997

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Abstract

The percentage of student failure in learning is still relatively high. Many countries, including Ghana, Nigeria, Enugu, and Indonesia, experience this condition. Internal and external factors that vary significantly between students have the potential to be the cause of failure. This condition cannot be allowed to continue. A special analysis is needed on the factors that can help improve student grades. Predictions of student success are urgently needed. These predictions can anticipate negative impacts that occur, including increased risk of dropout, decreased student motivation to learn, and decreased individual potential. The Naive Bayes and Decision Tree algorithms have been used to predict student success. However, despite their advantages, these two algorithms still have several weaknesses. It can cause the algorithm’s performance not to be as expected. Several methods in ensemble techniques can improve algorithm performance. Two methods that are often used are Bagging and AdaBoost. Bagging and AdaBoost can help improve the performance of classification algorithms. This study combines Bagging and AdaBoost into the Decision Tree and Naïve Bayes algorithms to optimize the results in predicting student success. The stages are data collection, pre-processing, data split, data processing, and evaluation model. The results show that the Bagging and AdaBoost techniques have been proven to be effective in improving accuracy, precision, recall, and F1-score performance. Combining the Naïve Bayes algorithm with AdaBoost significantly increases accuracy, precision, and F1-score by 1.95%, 28.98%, and 15.79%.

Item Type: Article
Subjects: Ilmu Komputer
eTheses
Divisions: Pasca Sarjana > Magister Teknik Informatika
Depositing User: Endi Febriyanto
Date Deposited: 13 Aug 2025 06:33
Last Modified: 13 Aug 2025 06:33
URI: http://repo.darmajaya.ac.id/id/eprint/20981

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