Responsible GeoAI untuk Pemetaan Risiko Bencana: Kerangka Tata Kelola Etis dalam Pengambilan Keputusan Pemerintah Daerah

Authors

  • Lalu Ahmad Murdhani Institut Pemerintahan Dalam Negeri

Keywords:

responsible GeoAI; disaster risk mapping; local government; spatial justice; ethical governance

Abstract

This study aims to analyze the opportunities and risks of using geospatial artificial intelligence, or GeoAI, in disaster risk mapping and to formulate an ethical responsible GeoAI governance framework for local government decision-making. This research employed a qualitative approach using a conceptual-analytical case study method. Data were collected through in-depth interviews, limited discussions, and document analysis of regulations, disaster management documents, risk maps, geospatial data policies, and relevant academic publications. The data were analyzed thematically through data reduction, theme categorization, data presentation, interpretation, and framework formulation. The findings show that GeoAI offers strategic opportunities to strengthen disaster risk mapping through the integration of spatial data, satellite imagery, remote sensing, demographic data, socio-economic data, infrastructure data, and environmental data. GeoAI can support the identification of hazard-prone areas, vulnerable groups, mitigation priorities, evacuation routes, risk-based budgeting, and pre-disaster action. However, the use of GeoAI also presents ethical risks, including spatial bias, unequal territorial representation, location privacy violations, algorithmic opacity, and limited community participation in validating risk maps. The main contribution of this study is the formulation of an ethical responsible GeoAI governance framework consisting of seven components: geospatial data governance, model transparency, algorithmic accountability, field validation and public participation, location privacy protection, spatial justice, and continuous adaptive evaluation. This framework positions GeoAI as a spatial decision-support system that is rapid, ethical, inclusive, and remains under meaningful human control.

 

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References

Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., Mahfud, C., Sinapoy, M. S., Djalante, S., Rafliana, I., Gunawan, L. A., Surtiari, G. A. K., & Warsilah, H. (2020). Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6, 100091. https://doi.org/10.1016/j.pdisas.2020.100091

Faadhilah, A., & Nugroho, H. (2024). Pemetaan daerah rawan longsor di Kabupaten Bandung Barat menggunakan metode machine learning dengan teknik SVM. Rekayasa Hijau: Jurnal Teknologi Ramah Lingkungan, 8(2).

Haris, N., Furqan, A. C., Kahar, A., & Karim, F. (2023). Disaster risk index on disaster management budgeting: Indonesia’s national data set. Jàmbá: Journal of Disaster Risk Studies, 15(1), Article a1365. https://doi.org/10.4102/jamba.v15i1.1365

Hayuningsih, D., Awaluddin, M., & Nugraha, A. L. (2024). Analisis ancaman kekeringan menggunakan metode Analytical Hierarchy Process berbasis Sistem Informasi Geografis di Kabupaten Sragen. TEKNIK, 45(2), 235–244. https://doi.org/10.14710/teknik.v45i2.58450

Hulu, A. E., R, D. A., Alexis, M., Arianingsih, I., Hamka, H., Purnama, R., & Maiwa, A. (2025). Pemodelan spasial kerawanan banjir di Kepulauan Nias dan sekitarnya berbasis Sistem Informasi Geografis dan Multi-Criteria Decision Analysis. Jurnal Ilmu Lingkungan, 23(5), 1243–1252. https://doi.org/10.14710/jil.23.5.1243-1252

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493

Lassa, J. A., Nappoe, G. E., & Sulistyo, S. B. (2022). Creating an institutional ecosystem for cash transfer programmes in post-disaster settings: A case from Indonesia. Jàmbá: Journal of Disaster Risk Studies, 14(1), Article a1046. https://doi.org/10.4102/jamba.v14i1.1046

Novianto, N. (2023). Models of digital transformation in the public sector. Policy & Governance Review, 7(2), 113–134. https://doi.org/10.30589/pgr.v7i2.753

Paramita, A., Suharyanto, & Wijaya, A. P. (2021). Pemetaan bahaya tsunami berbasis Sistem Informasi Geografis sebagai dasar mitigasi bencana di wilayah pesisir. Jurnal Geodesi Undip.

Triyanti, A., Surtiari, G. A. K., Lassa, J., Rafliana, I., Hanifa, N. R., Muhidin, M. I., & Djalante, R. (2023). Governing systemic and cascading disaster risk in Indonesia: Where do we stand and future outlook. Disaster Prevention and Management, 32(1), 27–48. https://doi.org/10.1108/DPM-07-2022-0156

Yuwono, B. D., Sabri, L. M., Wijaya, A. P., & Awaluddin, M. (2024). Assessment of flood risk induced by land subsidence using machine learning. Indonesian Journal of Geography. https://doi.org/10.22146/ijg.94726

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Published

30-09-2025

How to Cite

Murdhani, L. A. (2025). Responsible GeoAI untuk Pemetaan Risiko Bencana: Kerangka Tata Kelola Etis dalam Pengambilan Keputusan Pemerintah Daerah. Jurnal Perlindungan Masyarakat: Bestuur Praesidium, 2(2), 103–117. Retrieved from https://ejournal.ipdn.ac.id/jpa/article/view/6352