Artificial Intelligence for Detecting Oil Spills in Strategic Areas: A Strategic Study and Implementation Review in the Natuna Sea - Triyani, Triyani; Yulianto, Bayu Asih; Suwarno, Panji (Asian Journal of Social and Humanities, 2025)
Oil spills in the strategic area of Natuna have continued to increase in recent years, causing significant ecological, economic, and geopolitical impacts. However, Indonesia's marine pollution detection system still relies on conventional methods that are limited spatially, temporally, and operationally. This study aims to analyze the potential implementation of Artificial Intelligence, especially the Convolutional Neural Network (CNN) model based on satellite imagery, to improve the effectiveness of oil spill monitoring and strengthen national Maritime Situational Awareness (MSA). The research uses a qualitative approach through literature studies, strategic analysis, and Natuna case studies. Secondary data were collected from scientific articles, reports from national institutions, international platforms such as EMSA and Copernicus, and data on Natuna pollution incidents for the 2019–2023 period. The analysis was carried out through thematic analysis, comparative analysis, strategic analysis, and case-based reasoning. The results show that CNN has high accuracy in detecting oil spill patterns from Sentinel-1 and Sentinel-2 imagery and has the potential to provide valid digital documentation for rapid response and legal proceedings. Further analysis revealed that AI integration is technically feasible for application in the TNI Pusinformar system, although it requires strengthening infrastructure, human resources, SOPs, and data security. The discussion emphasizes that AI application can improve data-based diplomacy, environmental law enforcement, and Indonesia's maritime surveillance capacity; thus, a national roadmap and supporting policies are needed for operational implementation.