Optimalisasi Random Forest untuk Sentimen Pilkada Jawa Timur dengan Chi-Square dan Mutual Information - Rahma Putri Widyaiswari; Anisa Dzulkarnain; Alqis Rausanfita (JUITA: Jurnal Informatika, 2025)
The rise of social media has transformed the way people express opinions, including in political contexts. In the 2024 East Java Gubernatorial Election, social media platform X became a major outlet for public sentiment toward the governor and deputy governor candidates. This study aims to analyse public sentiment toward three candidate pairs by categorizing the data into three sentiment classes: positive, negative, and neutral. Feature selection was conducted by combining Term Frequency-Inverse Document Frequency (TF-IDF) with Chi-Square and Mutual Information (MI) methods to improve feature quality. The Random Forest algorithm was employed as the primary classification model. In addition, several other algorithms were tested for comparison. The results indicate that the TF-IDF and Chi-Square combination with Random Forest achieved the highest accuracy of 82.07%. These findings highlight the importance of feature selection in improving model performance for sentiment classification. The study provides insights into public opinion that can serve as a reference for strategic decision-making in the political and public sectors.