Analisis sentimen publik di twitter terhadap pelantikan presiden Prabowo menggunakan algoritma Naïve Bayes - Widyassari, Adhika Pramita; Salsabilla, Dea; Amrozi, M. Ali (NERO (Networking Engineering Research Operation), 2025)
Sentiment analysis on social media, especially Twitter, is an effective method to understand public opinion towards political events such as the inauguration of President Prabowo. This study aims to identify the sentiment of the Indonesian people regarding the inauguration of President Prabowo through machine learning-based sentiment analysis using the Multinomial Naïve Bayes algorithm. Data was collected from Twitter with relevant keywords and hashtags, covering the time span before and after the inauguration to capture the dynamics of sentiment changes. The preprocessing process was carried out through text cleaning, removing stop words, tokenization, and stemming to improve model accuracy. The classification results show the distribution of public sentiment, with the majority being neutral (52.63%), followed by positive sentiment (42.98%), and negative (4.39%). The model achieved an accuracy of 75%, showing quite good performance for short text classification. The contribution of this study lies in the application of sentiment analysis to the specific event of the inauguration of the President of Indonesia, with a focus on the critical period before and after the inauguration, which has not been widely studied before. The novelty of this study is the use of real-time Twitter data related to current political events (inauguration of President Prabowo), as well as the emphasis on neutral sentiment which provides a deeper dimension to public understanding. It is hoped that these findings can be the basis for designing more effective public communication strategies on social media.Keywords: naïve bayes, prabowo presidential inauguration, twitter, sentiment analysis