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

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Law Politic and Humanities

DOI: 10.38035/jlph.v5i6.2295

Authors: Santi Dwi Desy Lestari, Imam Yuadi

Institution: Universitas Airlangga

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: diamond

OpenAlex ID: W4414403873

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