Adoption of Artificial Intelligence for Human Capital Forecasting on Government Report Dota at SatLinMas under Satpol PP, Indonesia

Abstract

This study investigates how Generative AI (GenAI) can be employed as a methodological innovation to extract and analyze human-capital information from fragmented Satlinmas and Satpol PP administrative reports across Indonesian regions. Using a three-phase AI-enabled process—comprising automated text extraction, competency clustering, and scenario-based manpower forecasting—the research reveals structural human-capital challenges, including uneven manpower distribution, aging volunteer-based personnel, limited training access, and the absence of standardized competency documentation. The competency mapping identifies three dominant capability clusters: operational patrol and event security, disaster-response capacity, and community mediation. Experimental scenario testing demonstrates that even qualitative, text-derived indicators can support directional manpower projections, showing that regions with high operational intensity benefit most from expanded training and digital reporting, while aging rural units require targeted recruitment strategies. Beyond generating these insights, the study contributes to the broader discourse on AI adoption by illustrating how GenAI can serve as both a diagnostic tool and an analytic engine in low-data public-sector environments. While findings remain constrained by inconsistent reporting formats and the absence of formal HR datasets, this research provides a replicable framework for future scholars seeking to integrate AI into human-capital planning and institutional capacity building within safeguarding and governance institutions.