Optimizing Early Conflict Detection: The Role of Digital Technology in BHABINKAMTIBMAS Operational Learning
DOI:
https://doi.org/10.20961/ijolii.v3i02.3011Keywords:
BHABINKAMTIBMAS, Digital Technology, Early Conflict Detection, Educational Technology, Operational LearningAbstract
The integration of digital technology into the operational learning of BHABINKAMTIBMAS (Public Security and Order Officer) is a transformative strategy to enhance early social conflict detection. This study evaluates how digital tools function as pedagogical mediums that reshape officers' analytical competencies. The research follows a literature review methodology, involving three strategic steps: 1) identifying high-impact literature from 2014–2024, 2) synthesizing data using a thematic matrix (technical, instructional, and social outcomes), and 3) validating findings through cross-case triangulation. Results indicate that digital platforms, such as the Binmas Online System (BOS) and GIS mapping, increase detection response speed by 30% and prediction accuracy by 5.2%. The effectiveness of this technology-enhanced learning is significantly governed by officers' digital literacy and institutional support. In conclusion, optimizing early detection requires a shift from technical tool usage toward a comprehensive digital competency framework. This study contributes to the field of Educational Technology by providing a strategic model for Operational Learning in professional security training, offering a novel perspective on how digital integration can bridge the gap between field intelligence and instructional innovation in policing.
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