Social media platforms serve as dynamic communication across boundaries, with X serving as a platform for opinion exchange. This research examines public sentiment on the 2024 Indonesian Presidential Election to understand voter sentiments based on what happened during the pre-election. Using the Twitter API, 2,146 tweets were collected based on election-related keywords and hashtags, focusing on Indonesian-language tweets with direct opinions. Method that we use is data crawling using twitter API, preprocessing steps included case folding (converting text to lowercase), cleansing (removing noise like URLs and emojis), tokenization, stemming (reducing words to base forms), and stop word removal (e.g., “yang,” “dan”). Slang was standardized with custom dictionary to ensure consistency and accurate interpretation. Leveraging BERT for sentiment analysis, the model achieved 99% accuracy, results indicate that 93.1% of analysed tweets expressed negative sentiment, highlighting public dissatisfaction about the 2
