Optimizing the Intelligence Cycle in Migration Governance: The Transition Toward Early-Warning-Based Immigration Monitoring in Bali Province

The rapid increase in international tourist arrivals in Bali has intensified the complexity of immigration governance, particularly regarding the misuse of visit visas by foreign nationals. While immigration supervision has traditionally focused on administrative enforcement, evolving patterns of visa abuse involving unauthorized employment, nominee business practices, and hidden economic activities require a more proactive intelligence-based approach. This study aims to analyze the optimization of the immigration intelligence cycle in strengthening early-warning-based monitoring of foreign nationals in Bali Province. A qualitative research design was employed through semi-structured in-depth interviews with immigration officers, analysis of immigration regulations and policy documents, and examination of relevant media reports. Data were analyzed using the interactive model of Miles, Huberman, and Saldaña, supported by source triangulation to enhance credibility. The findings reveal that current immigration supervision remains predominantly reactive, relying on public reports and post-incident investigations rather than predictive threat detection. The study identifies an "iceberg phenomenon," where detected violations represent only a small proportion of actual irregular activities. To address this limitation, the research proposes an Early Warning Immigration Intelligence Model integrating multi-source intelligence, risk analysis, intelligence sharing, and preventive immigration actions within the intelligence cycle. The study concludes that strengthening early-warning-based immigration intelligence can improve proactive surveillance, enhance interagency coordination, and contribute to national resilience by preventing immigration-related threats before they escalate into broader economic, social, and security risks.