Strategi Transformasi Digital Sistem Informasi Geografis Nasional dalam Mendukung Keamanan Maritim Indonesia Menuju Indonesia Emas 2045
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Indonesia menghadapi tekanan ganda dalam pengelolaan wilayah laut dengan luas yurisdiksi maritim yang mencapai lebih dari 6,4 juta km² berhadapan dengan keterbatasan armada, personel, dan sistem pengawasan yang belum terintegrasi secara menyeluruh. Praktik illegal, unreported, and unregulated (IUU) fishing, pelanggaran jalur pelayaran oleh kapal asing, serta aktivitas grey zone di Laut Natuna Utara memperlihatkan bahwa kedaulatan maritim tidak lagi cukup dijaga dengan kekuatan fisik semata, melainkan memerlukan kapasitas deteksi dan analisis spasial yang presisi. Sistem Informasi Geografis (SIG) nasional berperan sebagai tulang punggung kemampuan tersebut, namun transformasi digitalnya masih berjalan tersendat akibat fragmentasi data antarlembaga, kesenjangan infrastruktur, dan minimnya sumber daya manusia yang menguasai teknologi geospasial mutakhir. Artikel ini disusun melalui pendekatan library research untuk mengkaji strategi transformasi digital SIG nasional sebagai penopang keamanan maritim dalam koridor Rencana Pembangunan Jangka Panjang Nasional 2025–2045 menuju visi Indonesia Emas 2045. Kajian menelusuri tantangan interoperabilitas data, kesiapan kelembagaan, kebutuhan kompetensi sumber daya manusia, risiko keamanan siber data geospasial, serta peluang integrasi kecerdasan buatan, big data, Internet of Things, digital twin, dan GeoAI dalam membentuk ekosistem geospasial maritim yang adaptif. Hasil kajian dirumuskan menjadi peta jalan implementasi tiga fase hingga tahun 2045 beserta rekomendasi kebijakan lintas kementerian dan lembaga. Artikel ini memberikan kontribusi berupa kerangka konseptual yang menghubungkan transformasi digital geospasial dengan agenda ketahanan nasional secara terpadu, sesuatu yang selama ini dibahas secara terpisah dalam literatur SIG dan literatur keamanan maritim Indonesia.
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Bachmann, N., Tripathi, S., Brunner, M., & Jodlbauer, H. (2022). The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals. Sustainability. https://doi.org/10.3390/su14052497
Batty, M. (2024). Digital twins in city planning. Nature Computational Science, 4, 192–199. https://doi.org/10.1038/s43588-024-00606-7
Broo, D. G., & Schooling, J. (2021). Digital twins in infrastructure: definitions, current practices, challenges and strategies. International Journal of Construction Management, 23, 1254–1263. https://doi.org/10.1080/15623599.2021.1966980
Costa, D. G., Bittencourt, J. C. N., Oliveira, F., Peixoto, J. P. J., & Jesus, T. C. (2024). Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches. Sustainability. https://doi.org/10.3390/su16020640
Dangermond, J., & Goodchild, M. (2019). Building geospatial infrastructure. Geo-Spatial Information Science, 23, 1–9. https://doi.org/10.1080/10095020.2019.1698274
Das, D. K. (2024). Exploring the Symbiotic Relationship between Digital Transformation, Infrastructure, Service Delivery, and Governance for Smart Sustainable Cities. Smart Cities. https://doi.org/10.3390/smartcities7020034
Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F. A., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. The Science of the Total Environment, 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033
Initiative, I. O. J. (2024). Membangun Sistem Keamanan Laut Indonesia untuk Mengawal Target-Target Pembangunan Jangka Panjang Indonesia 2025-2045. Indonesia Ocean Justice Initiative. https://oceanjusticeinitiative.org/id/membangun-sistem-keamanan-laut-indonesia-untuk-mengawal-target-target-pembangunan-jangka-panjang-indonesia-2025-2045/
Koldasbayeva, D., Tregubova, P., Gasanov, M., Zaytsev, A., Petrovskaia, A., & Burnaev, E. (2024). Challenges in data-driven geospatial modeling for environmental research and practice. Nature Communications, 15. https://doi.org/10.1038/s41467-024-55240-8
Li, Z., & Ning, H. (2023). Autonomous GIS: the next-generation AI-powered GIS. International Journal of Digital Earth, 16, 4668–4686. https://doi.org/10.1080/17538947.2023.2278895
Mortaheb, R., & Jankowski, P. (2022). Smart city re-imagined: City planning and GeoAI in the age of big data. Journal of Urban Management. https://doi.org/10.1016/j.jum.2022.08.001
Nadhila, M. S. (2023). Strategi Penanganan Keamanan Maritim Indonesia. Spektrum, 20(1).
Pandey, P. C., & Pandey, M. (2023). Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals. Sustainable Development. https://doi.org/10.1002/sd.2600
PPN/Bappenas, K. (2024). Undang-Undang Nomor 59 Tahun 2024 tentang Rencana Pembangunan Jangka Panjang Nasional 2025-2045. Kementerian PPN/Bappenas. https://www.bappenas.go.id/
Quamar, M. I., Al-Ramadan, B., Khan, K. A., Shafiullah, M., & El-Ferik, S. (2023). Advancements and Applications of Drone-Integrated Geographic Information System Technology - A Review. Remote Sensing, 15, 5039. https://doi.org/10.3390/rs15205039
Scott, G., & Rajabifard, A. (2017). Sustainable development and geospatial information: a strategic framework for integrating a global policy agenda into national geospatial capabilities. Geo-Spatial Information Science, 20, 59–76. https://doi.org/10.1080/10095020.2017.1325594
Sharma, S., Beslity, J. O., Rustad, L., Shelby, L. J., Manos, P. T., Khanal, P., Reinmann, A., & Khanal, C. (2024). Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data. Remote Sensing, 16, 4161. https://doi.org/10.3390/rs16224161
Wang, S., Hu, T., Xiao, H., Li, Y., Zhang, C., Ning, H., Zhu, R., Li, Z., & Ye, X. (2024). GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review. International Journal of Digital Earth, 17. https://doi.org/10.1080/17538947.2024.2353122
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10, 13–53. https://doi.org/10.1080/17538947.2016.1239771
Yang, Y., Shao, Z., Hu, Y., Mei, Q., Pan, J., Song, R., & Wang, P. (2022a). Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2022.113106
Yang, Y., Shao, Z., Hu, Y., Mei, Q., Pan, J., Song, R., & Wang, P. (2022b). Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2022.113106