Social media sentiment analysis is an effective tool for understanding public perceptions of political leaders’ policies and actions. This study aims to apply data mining and sentiment analysis techniques to tweets on Twitter during the first hundred days of President Prabowo Subianto’s leadership. Data was collected using the Twitter API and processed through several stages, including text pre-processing, feature extraction, and sentiment labeling. The methods used include sentiment analysis with the Naïve Bayes and Support Vector Machine (SVM) algorithms, as well as Natural Language Processing (NLP) techniques for further analysis. This approach is very effective in processing and predicting text-based data. The results of this study are expected to provide in-depth insights into public sentiment, both positive, negative, and neutral, as well as the factors that influence public opinion. These findings can be a reference for policy makers in understanding the dynamics of public opinion and directing more effective communication strategies.