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Journal of Social Science Utilizing Technology
Published by Yayasan Adra Karima Hubbi
ISSN : 30265959     EISSN : 3026605X     DOI : 10.70177/jssut
Core Subject :
Journal of Social Science Utilizing Technology focused on new research addressing Information, Management, Educational Technology, eLearning, Media Management, Human Resources, Fine and Applied Arts, Humanities Social Sciences, General and CrossDisciplinary and Communications Technologies as Applied to the Digital Worlds of Business, Government and Non-Governmental Enterprises. The Journal publishes work from all disciplinary, theoretical and methodological perspectives. It is designed to be read by researchers, scholars, teachers and advanced students in the fields of Information Systems, Management and Information Science, as well as information technology developers, consultants, software vendors, and senior business and information technology executives seeking an update on current experience and future prospects in relation to contemporary information and communications technologies.
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Articles 131 Documents
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Big Data Analytics and AI-Driven Maritime Intelligence: Transforming Public Policy for Hybrid Threat Mitigation in the North Natuna Sea Dinar Jati Kusuma; Rudy Sokabla; I Gusti Ketut Hans Desember
Journal of Social Science Utilizing Technology Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v4i4.4536

Abstract

Background. The North Natuna Sea faces overlapping maritime security challenges involving contested activities, fragmented surveillance information, and emerging hybrid threats. These conditions demand timely intelligence and coordinated public policy without compromising legal accountability. Purpose. This study aims to examine how big data analytics and artificial intelligence can support maritime intelligence integration and inform responsive policies for hybrid threat mitigation. Method. A mixed-methods research framework is proposed, combining policy document analysis, stakeholder interviews, and quantitative evaluation of anonymized maritime surveillance indicators. The analytical approach integrates data quality assessment, predictive modeling, and thematic analysis to evaluate institutional coordination, decision support, and governance safeguards. Results. The proposed framework identifies potential benefits in detecting anomalous maritime patterns, improving information sharing, and prioritizing policy responses. However, its anticipated constraints include data fragmentation, algorithmic uncertainty, institutional interoperability, and risks to transparency and privacy. (Note: These propositions remain unverified until empirical analysis is completed). Conclusion. The study concludes that AI-enabled maritime intelligence should complement, rather than replace, human judgment and established legal procedures. Effective adoption requires interoperable data standards, accountable oversight, rigorous model validation, and sustained interagency collaboration. The framework offers a testable basis for strengthening evidence-informed maritime governance while avoiding unsupported claims about operational effectiveness or threat reduction in the North Natuna Sea.

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