<![CDATA[The controversy surrounding the Jakarta-Bandung High-Speed Rail (Whoosh) project’s debt has triggered significant public opinion polarization on Facebook. This research aims to analyze public sentiment regarding this financial issue to transform unstructured data into strategic insights for stakeholders. The applied methodology follows the CRISP-DM framework using the Naïve Bayes Classifier algorithm reinforced by Term Frequency-Inverse Document Frequency (TF-IDF) weighting. Primary data consisted of 2,000 comments from the "Nusa Update" fan page, which underwent thorough text preprocessing including cleansing, normalization, and stemming to reduce noise. Results indicate that the classification model achieved an overall accuracy of 83.75%, exceeding the 80% target. Sentiment distribution analysis reveals a dominance of negative opinions at over 80%, reflecting public concern over cost overruns and the burden on the state budget (APBN). These findings are implemented into an interactive Streamlit-based dashboard to facilitate real-time monitoring of public policy reputation. This study confirms the effectiveness of hybrid approaches in transforming "digital waste" into measurable scientific information.]]>
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