Integrasi Sistem Informasi Geospasial untuk Penguatan Keamanan Maritim Indonesia: Perspektif Decision Support System dan Transformasi Digital
Article Sidebar
Main Article Content
Abstract
Wilayah laut Indonesia yang sangat luas menghadirkan tantangan pengawasan yang tidak sebanding dengan kapasitas metode konvensional yang selama ini digunakan, sementara ancaman seperti illegal fishing, penyelundupan, perdagangan manusia, pencemaran laut, dan pelanggaran wilayah terus berlangsung di wilayah laut Indonesia. Penelitian ini bertujuan menganalisis peran Sistem Informasi Geografis (SIG) sebagai instrumen pendukung keputusan dalam mendukung keamanan maritim nasional. Penelitian menggunakan pendekatan kualitatif deskriptif berbasis studi pustaka, dengan sumber data berupa artikel jurnal ilmiah terindeks Scopus kategori Q1 dan Q2 yang relevan dengan tema keamanan maritim dan teknologi geospasial. Hasil kajian menunjukkan bahwa SIG telah bergeser fungsinya dari sekadar alat pemetaan menjadi Decision Support System yang mengintegrasikan data spasial dan nonspasial dari berbagai sumber, termasuk citra satelit, sistem identifikasi otomatis kapal, radar, dan sensor wahana tanpa awak, sehingga membentuk kesadaran situasional laut yang lebih menyeluruh. Penerapan analisis spasial pada studi kasus Laut Natuna Utara memperlihatkan bahwa pemetaan kerawanan berbasis data mampu mengidentifikasi wilayah yang selama ini luput dari pengawasan meskipun tidak menunjukkan riwayat pelanggaran yang tinggi. Rancangan sistem informasi geospasial yang terintegrasi lintas instansi terbukti dapat mengurangi duplikasi data dan memperkuat koordinasi antarlembaga keamanan maritim. Selain itu, disusun pula strategi transformasi digital jangka panjang yang mencakup penguatan infrastruktur teknologi, kesiapan sumber daya manusia, kebijakan nasional, keamanan siber, serta integrasi dengan kecerdasan buatan, big data, Internet of Things, komputasi awan, dan digital twin, sebagai kerangka menuju Indonesia Emas 2045. Penelitian ini merekomendasikan pembentukan badan koordinasi data geospasial maritim nasional serta pergeseran strategi pengawasan dari pola konvensional menuju pendekatan berbasis analisis risiko keruangan.
Article Details
Section
How to Cite
References
Abdul Rahman, S., Shaharuddin, S., Shapiai, M., & Ahmad, A. (2026). From BIM and geospatial technology to AI-driven digital twins: Integrating policy, geospatial intelligence, and technological synergies in Malaysia. International Journal of Geoinformatics, 22(1), 12–23. https://doi.org/10.52939/ijg.v22i1.4713
Adi, A. P., Aritonang, S., Sarjito, S., & Navalino, R. D. A. (2026). A risk-based geospatial optimization framework for UAV maritime surveillance path planning in the North Natuna Sea. International Journal of Geoinformatics, 22(3), 169–187. https://doi.org/10.52939/ijg.v22i3.4877
Brodeur, J., Bédard, Y., & Moulin, B. (2005). A geosemantic proximity-based prototype for the interoperability of geospatial data. Computers, Environment and Urban Systems, 29(6), 669–698.
Dold, J., & Groopman, J. (2017). The future of geospatial intelligence. Geo-Spatial Information Science, 20(2), 151–162. https://doi.org/10.1080/10095020.2017.1337318
Drummond, W. J., & French, S. P. (2008). The future of GIS in planning: Converging technologies and diverging interests. Journal of the American Planning Association, 74(2), 161–174.
Foglini, F., & Grande, V. (2023). A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective: The Adriatic Sea experience. Oceanologia, 65(1), 260–277. https://doi.org/10.1016/j.oceano.2022.11.002
Forti, N., d’Afflisio, E., Braca, P., Millefiori, L. M., Carniel, S., & Willett, P. (2022). Next-gen intelligent situational awareness systems for maritime surveillance and autonomous navigation [Point of View]. Proceedings of the IEEE, 110(10), 1532–1537.
Handrisal, H., Armawi, A., Triyana, H. J., & Arsana, I. M. A. (2026). From enforcement to resilience: Evaluating Indonesia’s one roof enforcement system to combat illegal fishing in the North Natuna Sea. F1000Research, 15, 709.
Karountzos, O., Kagkelis, G., & Kepaptsoglou, K. (2023). A decision support GIS framework for establishing zero-emission maritime networks: The case of the Greek coastal shipping network. Journal of Geovisualization and Spatial Analysis, 7(16). https://doi.org/10.1007/s41651-023-00145-1
Le Tixerant, M., Le Guyader, D., Gourmelon, F., & Queffelec, B. (2018). How can Automatic Identification System (AIS) data be used for maritime spatial planning? Ocean & Coastal Management, 166, 18–30.
Li, R., & Saxena, N. K. (1993). Development of an integrated marine geographic information system. Marine Geodesy, 16(4), 293–307.
Li, Z., Tang, W., Huang, Q., Shook, E., & Guan, Q. (2020). Introduction to big data computing for geospatial applications. ISPRS International Journal of Geo-Information, 9(7), 487.
Masser, I. (1999). All shapes and sizes: The first generation of national spatial data infrastructures. International Journal of Geographical Information Science, 13(1), 67–84.
Ou, Z., & Zhu, J. (2008). AIS database powered by GIS technology for maritime safety and security. The Journal of Navigation, 61(4), 655–665.
Ray, C., Dreo, R., Camossi, E., Jousselme, A.-L., & Iphar, C. (2019). Heterogeneous integrated dataset for maritime intelligence, surveillance, and reconnaissance. Data in Brief, 25, 104141. https://doi.org/10.1016/j.dib.2019.104141
Rosana, N., Prasita, V. D., & Tambun, R. (2014). GIS for monitoring the operation on inspection and termination of fishing vessels in the Eastern Indonesian waters. The International Journal of Engineering and Science, 3(3), 20–28.
Shi, J., Pan, Z., Jiang, L., & Zhai, X. (2023). An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT. Advanced Engineering Informatics, 57, 102114.
Strain, L., Rajabifard, A., & Williamson, I. (2006). Marine administration and spatial data infrastructure. Marine Policy, 30(4), 431–441.
Talpur, K., Hasan, R., Gocer, I., Ahmad, S., & Bhuiyan, Z. (2025). AI in maritime security: Applications, challenges, future directions, and key data sources. Information, 16(8), 658. https://doi.org/10.3390/info16080658
Thompson, L. M., Vlietstra, L. S., Watson, J. R., & Harte, M. (2026). Improving maritime domain awareness using vessel location data in Bering Sea fisheries. Frontiers in Marine Science, 13, 1844404. https://doi.org/10.3389/fmars.2026.1844404
Verutes, G. M., Johnson, A. F., Caillat, M., Ponnampalam, L. S., Peter, C., Vu, L., Junchompoo, C., Lewison, R. L., & Hines, E. M. (2020). Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries. PLOS ONE, 15(8), e0237835.
Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., & Nyerges, T. L. (2013). CyberGIS software: A synthetic review and integration roadmap. International Journal of Geographical Information Science, 27(11), 2122–2145.
Zhao, M., Yao, X., Sun, J., Zhang, S., & Bai, J. (2018). GIS-based simulation methodology for evaluating ship encounters probability to improve maritime traffic safety. IEEE Transactions on Intelligent Transportation Systems, 20(1), 323–337.
Zhou, X., Cheng, L., Li, W., Zhang, C., Zhang, F., Zeng, F., Yan, Z., Ruan, X., & Li, M. (2020). A comprehensive path planning framework for patrolling marine environment. Ocean Engineering, 198.