Utilizing Artificial Intelligence to Analyze Citizens Reactions toward BPJS Class Removal Policy: An Administrative Science Perspective

Abstract

This study explores public reactions to the Indonesian government's policy of removing class distinctions (kelas 1, 2, and 3) within the national health insurance program, BPJS Kesehatan. By utilizing sentiment analysis, emotion classification, word cloud visualization, and topic modeling, we analyze 1,497 citizen comments extracted from a viral YouTube discussion featuring Hotman Paris and Irma Hutabarat, with video ID WOJBhwY4zeM. The research adopts an interdisciplinary approach, integrating computational linguistics with administrative science to understand how citizens emotionally and cognitively respond to health policy changes. The results reveal a dominant discourse centered on inequality, access to hospital services, economic burden, and distrust in public administration. Emotion analysis indicates that “Surprise” and “Joy” are the most frequent emotions, though they often reflect sarcasm and disbelief rather than genuine positivity. Sentiment heatmaps and compound score analyses show a mixture of neutral, negative, and sarcasticpositive tones, signaling emotionally complex yet reflective civic engagement. Topic modeling confirms that key issues include room availability, hospital bureaucracy, and the need for equitable service allocation. The findings highlight significant communication gaps between policymakers and the public. Many citizens express confusion, fear, and a lack of trust, suggesting that the top-down communication strategy has not sufficiently addressed their concerns. From an administrative science perspective, this study underscores the importance of integrating AI-driven public sentiment monitoring into health policy evaluation. The results can inform more empathetic, transparent, and participatory policy-making processes to enhance the legitimacy and acceptance of major public health reforms.

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