KLASIFIKASI PENYAKIT BERBASIS BAGGING MENGGUNAKAN NAÏVE BAYES, DECISION TREE, DAN SUPPORT VECTOR MACHINE

IMMANUEL, JONATHAN and Yusliani, Novi and Saputra, Danny Matthew (2024) KLASIFIKASI PENYAKIT BERBASIS BAGGING MENGGUNAKAN NAÏVE BAYES, DECISION TREE, DAN SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

In classification, finding the optimal model to handle a specific problem is crucial. Various algorithms, such as Naïve Bayes, Decision Tree, and Support Vector Machines (SVM), each have their own strengths and weaknesses. One commonly used technique to enhance model performance is Bagging, the ensemble technique. Bagging combines weak models into a stronger model by reducing bias and variance. This research explores the application of the Bagging aggregation technique using Naïve Bayes, Decision Tree, and SVM for disease classification in the Multiple Disease Prediction dataset obtained from Kaggle. The purpose is to develop a Bagging-based classification system that can improve model performance and demonstrate an alternative method for enhancing model performance. After preprocessing the dataset, it was found that the model’s performance was subpar, so the training and testing sets were combined, shuffled, and split again to provide optimal conditions. The results show that Bagging improves model performance, especially the Bagged Decision Tree model, achieving the highest F1-Score of 0.971. However, other models did not show the same results. Models Bagged with Naïve Bayes and SVM showed improvement compared to the normal models, but the performance of the superior algorithm, Decision Tree, decreased, making the overall performance suboptimal. The largest performance drop was observed in the Bagged Naïve Bayes and SVM model, with a score of 0.625. The findings indicate that the Bagging-based classification system has been successfully implemented, but clear results regarding the improvement of model performance using the Bagging aggregation technique have not yet been obtained.

Item Type:	Thesis (Undergraduate)
Additional Information:	Tesis dibuat sebagai laporan artikel yang dipublikasi ke Prosiding Konferensi Internasional ICECOS 2024. Supplemental materials include copyright receipt from IEEE Proceeding Article Usage, proceeding articles provided by event hosters, and also similarity test of the thesis provided by Sriwijaya University Library.
Uncontrolled Keywords:	Klasifikasi, Bagging, Naïve Bayes, Decision Tree, Support Vector Machine
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Jonathan Immanuel
Date Deposited:	08 Jan 2025 04:33
Last Modified:	08 Jan 2025 04:33
URI:	http://repository.unsri.ac.id/id/eprint/160507

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