Artificial Intelligence and Big Data Analytics to Break Drug Networks: Lessons from Law Enforcement in Indonesia

Abstract

The rapid advancement of Artificial Intelligence (AI) and Big Data Analytics has transformed various sectors, including law enforcement and criminal investigation. This study aims to explore how Indonesian law enforcement agencies are utilizing AI and Big Data to combat drug trafficking networks. Using a qualitative approach, data was collected through in-depth interviews with five key informants, including law enforcement officials, legal experts, and technology practitioners. The results show that AI technologies, such as predictive analytics, surveillance systems, and facial recognition, have enhanced intelligence gathering, suspect identification, and crime pattern analysis. However, challenges remain, including limited technological infrastructure, lack of trained personnel, legal and ethical concerns, and weak inter-agency coordination. By examining global best practices from countries like the United States, China, and the European Union, the study provides recommendations to improve Indonesia's capacity in using AI and Big Data for drug enforcement. The findings contribute to theoretical frameworks such as Routine Activity Theory and Crime Pattern Theory, emphasizing the proactive role of technology in crime prevention. Strengthening infrastructure, legal frameworks, and collaboration across agencies is essential for the effective integration of AI and Big Data in Indonesia's war on drugs.

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