LEVERAGING BIG DATA FOR INDONESIA’S IMMIGRATION POLICY: OPPORTUNITIES AND LIMITATIONS

Indonesia's fragmented immigration data systems, exacerbated by its archipelagic geography and institutional complexity, pose significant challenges to effective immigration management. These issues result in inefficiencies in border security, difficulties in tracking irregular migration, and substantial economic losses due to undocumented migrant workers. For instance, discrepancies between official data and World Bank estimates reveal a gap of 5.3 million unrecorded migrant workers, highlighting systemic failures in data integration and enforcement. This study explores the potential of big data analytics to address these challenges by integrating disparate systems and enhancing decision-making processes. Using a normative juridical approach, the research examines Indonesia's legal frameworks and proposes a comprehensive implementation framework. This framework includes centralized data integration using Hadoop and Spark technologies, predictive analytics for migration patterns, and robust privacy safeguards to protect vulnerable populations. The findings emphasize that big data can significantly improve operational efficiency, enhance national security, and support evidence-based policy development. However, the study also identifies critical barriers such as privacy concerns, technical limitations, and institutional coordination gaps. By addressing these challenges, the proposed framework offers actionable insights into leveraging big data for effective immigration policing in Indonesia while safeguarding civil liberties.