<![CDATA[Across BNN, police, health, corrections, and local government, Indonesia's crossagen cynarcotics control produces a lot of data yet fragmented, limiting timely identification of abuse patterns,hotspots, and resource requirements. It aims to bridge the gap between international breakthroughson the use of machine-learning–based monitoring and optimization under uncertainty and underdeveloped provincial integration of governance, interoperability, and predictive analytics in the public sector. It is about designing and assessing an integrated spatiotemporal predictive, datadriven narcotics intelligence ecosystem for BNNP West Java. The approach combines iterative information systems engineering with an embedded case study and a mixed-methods evaluation covering seven phases: requirements structuring; data governance and quality; federated/hybrid interoperability and Privacy-Preserving Record Linkage; spatiotemporal predictive pipelines with both baseline and advanced models and anomaly detection; hotspot and risk mapping; early warning and situational dashboards linked to operational protocols; and implementation assessment with institutional learning.Evaluation utilizes quantitative measurements for data quality and model performance (including lead time and false-alarm considerations) and qualitative findings evaluating governance readiness and usability. Expected outputs can comprise a four-pillar framework bridging governance and policy impact, replicable artefacts to be deployed at the provincial level, and implications for evidence-based narcotics policy under national digital-government agendas, with considerations for data-access andprivacy limitations.]]>
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