Sentiment Analysis for the 2024 DKI Jakarta Gubernatorial Election Using a Support Vector Machine Approach - Angreni, Dwi Shinta; Nur, Sri Khaerawati; Jayanto, Deni Luvi; Mariani, Mariani; Rinianty, Rinianty (Politeknik Negeri Batam, 2025)
This study analyzes public sentiment regarding candidates in the 2024 DKI Jakarta Gubernatorial Election utilizing a Support Vector Machine (SVM) approach. Recognizing the pivotal role of social media, particularly Twitter, in shaping public opinion, the research addresses the challenges of processing large volumes of unstructured data. Through systematic data preprocessing and feature extraction, the SVM model was applied, achieving a sentiment classification accuracy of 70%. The analysis revealed a distribution of sentiments where 36.1% of comments were positive, 33.4% negative, and 30.5% neutral. These findings illustrate the complexities of public discourse surrounding key political events, highlighting the model's efficacy and the nuances of sentiment detection. Moreover, discussions on model limitations elucidate areas for enhancement, suggesting future avenues including the adoption of more sophisticated algorithms and improved data processing techniques. This research contributes to the understanding of voter sentiment dynamics in a significant electoral context, providing insights that may assist campaign strategies and political analyses in Indonesia.