Integrasi Artificial Intelligence dalam Manajemen Risiko Bencana Pemerintah Daerah: Peluang, Tantangan, dan Model Tata Kelola Adaptif
Keywords:
artificial intelligence; disaster risk management; local government; adaptive governance; algorithmic accountabilityAbstract
This study aims to analyze the opportunities and challenges of integrating artificial intelligence into local government disaster risk management and to formulate an adaptive governance model that is accountable and ethical. 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, digital transformation policies, disaster management documents, and relevant academic publications. The data were analyzed thematically through data reduction, theme categorization, data presentation, interpretation, and model formulation. The findings reveal that AI offers strategic opportunities to support risk mapping, disaster prediction, early warning systems, vulnerability analysis, intervention prioritization, and rapid evidence-based decision-making. However, AI integration also faces serious challenges, particularly data fragmentation, data quality, algorithmic bias, black-box models, personal data protection, cross-agency coordination, and limited bureaucratic capacity. The main contribution of this study is the formulation of an adaptive AI governance model consisting of six components: integrated data governance, AI as a decision-support system, algorithmic accountability, cross-sectoral coordination, ethical and data protection safeguards, and continuous adaptive evaluation. This model positions AI as an instrument of public management to strengthen disaster risk reduction in a rapid, accountable, and ethical manner while remaining under meaningful human control.
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Bhardwaj, J., Kuleshov, Y., Aitkenhead, I., & Asghari, A. (2021). Building capacity for a user-centred integrated early warning system for drought in the Northern Murray-Darling Basin. Natural Hazards, 107, 97–122. https://doi.org/10.1007/s11069-021-04575-2
Budimir, M., Šakić Trogrlić, R., Almeida, C., Arestegui, M., Vásquez, O. C., Cisneros, A., & others. (2025). Opportunities and challenges for people-centered multi-hazard early warning systems: Perspectives from the Global South. iScience. https://doi.org/10.1016/j.isci.2025.112353
Chun, K. P., Octavianti, T., Dogulu, N., Tyralis, H., Papacharalampous, G., Rowberry, R., Fan, P., Everard, M., Francesch-Huidobro, M., Migliari, W., Hannah, D. M., Marshall, J. T., Tolosana Calasanz, R., Staddon, C., Ansharyani, I., Dieppois, B., Lewis, T. R., Ponce, J., Ibrean, S., Ferreira, T. M., Peliño-Golle, C., Mu, Y., Delgado, M., Silvestre Espinoza, E., Keulertz, M., Gopinath, D., & Li, C. (2025). Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives. International Journal of Disaster Risk Reduction.
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
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
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
Kam, P. M., Ciccone, F., Kropf, C. M., Riedel, L., Fairless, C., & Bresch, D. N. (2024). Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action. Nature Communications, 15, Article 53200. https://doi.org/10.1038/s41467-024-53200-w
Kolivand, P., Azari, S., Bakhtiari, A., & others. (2025). AI applications in disaster governance with health approach: A scoping review. Archives of Public Health, 83, Article 218. https://doi.org/10.1186/s13690-025-01712-2
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
Marchezini, V. (2020). “What is a sociologist doing here?” An unconventional people-centered approach to improve warning implementation in the Sendai Framework for Disaster Risk Reduction. International Journal of Disaster Risk Science, 11, 218–229. https://doi.org/10.1007/s13753-020-00262-1
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
Sufri, S., Dwirahmadi, F., Phung, D., & Rutherford, S. (2020). A systematic review of community engagement in disaster early warning systems. Progress in Disaster Science, 5, 100058. https://doi.org/10.1016/j.pdisas.2019.100058
Tarchiani, V., Massazza, G., Rosso, M., Tiepolo, M., Pezzoli, A., Housseini Ibrahim, M., Katiellou, G. L., Tamagnone, P., De Filippis, T., Rocchi, L., Marchi, V., & Rapisardi, E. (2020). Community and impact based early warning system for flood risk preparedness: The experience of the Sirba River in Niger. Sustainability, 12(5), 1802. https://doi.org/10.3390/su12051802
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
Tzachor, A., Whittlestone, J., Sundaram, L., & Ó hÉigeartaigh, S. (2020). Artificial intelligence in a crisis needs ethics with urgency. Nature Machine Intelligence, 2, 365–366. https://doi.org/10.1038/s42256-020-0195-0
van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714. https://doi.org/10.1016/j.giq.2022.101714
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577. https://doi.org/10.1016/j.giq.2021.101577
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Copyright (c) 2025 Erfan Wahyudi, Lalu Ahmad Murdhani
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