Mapping Sentiment towards Danantara: A Combined Clustering and Text- Based Predictive Model

Research aims to map public sentiment towards Danantara with the integration of clustering and text-based predictive models from social media data. Clustering using K-means obtained three clusters namely political criticism, neutral and prositive support. Linear SVM model performed best with 96% accuracy, followed by random forest (93%), Logistic Regression (90%) and Naïve Bayes (83%). The findings confirm that the public is highly sensitive to issues of transparency and governance in the establishment of Danantara, and the need for a responsive, data-driven public communication strategy. This research contributes to the public opinion monitoring system for national strategic policies.