Comparative Analysis of Machine Learning Algorithms with SMOTE and Boosting Techniques in Accuracy Improvement

Abstract

This research explores and enhances accuracy in sentiment classification related to Indonesia's Capital City relocation by combining Naive Bayes (NB), Random Forest (RF), SMOTE, and XGBoost. The study addresses challenges of unbalanced data and complexity in social media sentiment analysis. The combination of RF with SMOTE achieved the highest accuracy at 91.25%, demonstrating SMOTE's effectiveness in balancing the dataset and improving minority class detection. While adding XGBoost slightly reduced accuracy (90.92%), it increased the NB model's accuracy from 77.45% to 85.97% when combined with SMOTE. RF alone reached 87.46% and improved to 88.78% with XGBoost. The study underscores the importance of selecting and combining techniques to maximize sentiment prediction accuracy. Future research could explore deep learning or transformer models for even better results, offering deeper insights into public sentiment and aiding effective policy strategy development.