Enhancing Machine Learning Model Performance in Addressing Class Imbalance

Lucky Lhaura Van FC (University of Lancang Kuning)
M. Khairul Anam (University of Samudra)
Muhammad Bambang Firdaus (University of Mulawarman)
Yogi Yunefri (University Of Lancang Kuning)
Nadya Alinda Rahmi (University of Putra Indonesia YPTK Padang)

CogITo Smart Journal
Vol. 10 No. 1 (2024): Cogito Smart Journal

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.