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Rizky A, Galih Prakoso
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Journal : Journal Basic Science and Technology

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AI-Based Sentiment Analysis of Social Media to Detect Public Opinion on Government Policies Rizky, Galih Prakoso; Alrasyid, Wildan
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

In the digital age, social media has become a powerful platform for public expression and discourse, offering governments a real-time window into citizen sentiment. This research explores the application of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) techniques, to analyze public sentiment on social media in response to government policies. Using data primarily sourced from Twitter, the study applies a BERT-based sentiment analysis model to classify public reactions into positive, negative, and neutral categories. The model achieved high performance with an accuracy of 89.2%, precision of 88.6%, and recall of 87.9%, outperforming traditional classifiers. Sentiment was analyzed across three key policy areas: fuel subsidy removal, education curriculum reform, and COVID-19 vaccination programs. Results indicate significant variations in public sentiment based on policy type, timing, and inferred demographic factors. A real-time sentiment analysis dashboard was developed to support policymakers in monitoring public opinion trends and improving communication strategies. This study demonstrates the potential of AI-driven sentiment analysis as a tool for enhancing data-informed governance, public engagement, and policy responsiveness.
Co-Authors Alrasyid, Wildan Bambang Saras Yulistiawan Budi Arif Dermawan Hengki Tamando Sihotang Mulianingtyas , RR Octanty Mulianingtyas, RR Octanty Nurul Afifah Arifuddin Rasenda, Rasenda Rifka Widyastuti, Rifka Widyastuti , Rifka