Developing an AI-Enhanced Maritime Threat Detection Model: : Predictive Security Framework for Illegal Fishing and Piracy in the Natuna Sea - Inayah, Atikah; Muhamad Risahdi; Desi Albert Mamahit (International Journal of Education, Vocational and Social Science, 2025)
Abstract This research proposes a predictive security framework based on artificial intelligence to counter non-traditional maritime threats such as illegal fishing and piracy in the geopolitically critical North Natuna Sea at the southern boundary of the South China Sea. The framework integrates multiple maritime data streams-including vessel tracking, satellite imagery, radar observations, and logbook entries-and applies advanced machine learning methods such as long short term memory networks and computer vision to identify anomalous vessel behaviour. The results indicate that the artificial intelligence system can reliably distinguish between lawful and illicit maritime operations, enabling the development of early warning capabilities. The study also highlights that the effectiveness of this predictive security approach relies on supportive regulatory frameworks and coordinated action among relevant agencies. This work presents a novel, context specific solution tailored to Indonesia’s maritime environment, combining behavioural vessel modelling, multisource data fusion, and geopolitical awareness. The proposed model facilitates a shift from reactive to proactive maritime threat management and offers a scalable approach applicable to other high risk maritime regions.