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Pengembangan Model Deteksi Ancaman Maritim Berbasis AI: Kerangka Keamanan Prediktif untuk Penangkapan Ikan Ilegal dan Pembajakan di Laut Natuna Triyani, Triyani; Supriyadi, A. Adang; yulianto, Bayu Asih; Yudho, Lukman; Suwarno, Panji
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 7 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i7.4513

Abstract

The North Natuna Sea, which is located in Indonesia's Exclusive Economic Zone, is increasingly vulnerable to various maritime threats such as illegal, unreported, and unregulated (IUU fishing), piracy, and territorial violations by foreign vessels, especially in the context of increasing geopolitical tensions in the South China Sea region. Conventional marine surveillance systems have proven inadequate in providing early warning or proactive prevention due to their limited range and reactive nature. This study aims to explore the potential use of artificial intelligence (AI) in strengthening Indonesia's maritime security system through predictive detection models. Based on a review of the growing literature on the application of AI in ocean monitoring and maritime risk management, this study examines how machine learning techniques have revolutionized marine operations in developed countries through data-driven navigation and intelligent surveillance. Methods: This study used a descriptive qualitative approach with case studies in the Natuna region, and was based on literature studies as well as secondary data such as Automatic Identification System (AIS) recordings, satellite imagery, and open-source maritime incident reports. Results: The results of the analysis show that a combination of algorithms such as Random Forest, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) can form a hybrid detection model that is able to recognize suspicious vessel behavior, predict risk zones, and provide early warning of IUU fishing activities. The study also highlights the importance of integrating AIS data, satellite imagery, and marine sensors to improve spatial awareness and response precision. Conclusion: AI-based maritime security provides a strategic opportunity for Indonesia to move from a reactive defense system to a predictive and anticipatory approach. Novelty of this article: The novelty in this study lies in the integration of the concepts of predictive security and preventive defense in the design of AI models that are specific to vulnerable areas such as the North Natuna Sea—an approach that has never been operationalized in real terms in the context of Indonesia's maritime security.