- Sentiment Analysis Implementation: Conducted a comprehensive sentiment analysis of public perception regarding the Jabodetabek LRT project using machine learning techniques, specifically employing the TextBlob library. - Model Evaluation: Achieved a model accuracy of 90.35%, with detailed performance metrics including precision, recall, and F1-scores for sentiment categories (positive, neutral, negative), demonstrating strong predictive capabilities. - Data Visualization: Created informative visualizations, including pie charts, to effectively communicate sentiment distribution and insights derived from the analysis to publics. - Insights for Publics: Provided actionable insights for policymakers and transportation authorities based on public sentiment, helping to identify areas for improvement in the LRT services and enhance public satisfaction.