Analysis Of Indonesian People's Sentiment Towards 2024 Presidential Candidates On Social Media Using Naïve Bayes Classifier and Support Vector Machine - Mardiah, Nia; Marlina, Leni; Khairul, Khairul; Sitorus, Zulham; Iqbal, Muhammad (Building of Informatics, Technology and Science (BITS), 2024)
This research aims to analyze the sentiment of the Indonesian public towards the 2024 presidential candidates on social media platforms X and Instagram. The main issue addressed is how to determine public opinion as disseminated on social media regarding the presidential candidates. To address this issue, two classification methods are used: Naïve Bayes Classifier and Support Vector Machine (SVM). The objective of this research is to measure public sentiment, both positive and negative, towards the 2024 presidential candidates using these two methods. The research findings indicate that the implementation of the Naïve Bayes method with manual labeling achieved the highest accuracy of 86% for X data and 85% for Instagram comments data. Meanwhile, with lexicon-based labeling, the highest accuracy was 60% for both X and Instagram data. The SVM method with manual labeling also achieved the highest accuracy of 86% for X data and 85% for Instagram data. With lexicon-based labeling, the highest accuracy was 60% for X data and 70% for Instagram data. This research concludes that both Naïve Bayes and SVM demonstrate strong performance in sentiment analysis on social media, with SVM slightly outperforming in some scenarios. The implementation of these two methods provides valuable insights into public opinion towards the 2024 presidential candidates on social media.