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All Journal CogITo Smart Journal
Muhammad Bambang Firdaus
University of Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Enhancing Machine Learning Model Performance in Addressing Class Imbalance Lucky Lhaura Van FC; M. Khairul Anam; Muhammad Bambang Firdaus; Yogi Yunefri; Nadya Alinda Rahmi
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.626.478-490

Abstract

This research aims to investigate methods for handling class imbalance in machine learning models, with a focus on the Support Vector Machine (SVM) algorithm. We apply oversampling (SMOTE) and undersampling techniques to a dataset with class imbalance and evaluate the performance of SVM using these methods. Experiments are conducted using data from Twitter social media regarding the 2024 general electionsThe findings indicate that incorporating SMOTE effectively enhances the performance of SVM models, particularly within the SVM Polynomial variant. However, the use of undersampling shows limited impact on improving SVM model performance. This study provides valuable insights for researchers and practitioners in choosing the appropriate strategy for handling class imbalance in machine learning models.
Co-Authors Anam, M Khairul Lucky Lhaura Van FC Nadya Alinda Rahmi Yogi Yunefri, Yogi