PEMANFAATAN ARTIFICIAL INTELLIGENCE (AI) UNTUK MENINGKATKAN MITIGASI BENCANA BANJIR DI INDONESIA
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Indonesia is among the countries with the highest risk of natural disasters, particularly floods, which frequently impact both urban and rural areas. The increasing intensity and frequency of floods are caused by a combination of poor spatial planning, climate change, and weak early warning systems. This article aims to explore the role of Artificial Intelligence (AI) in enhancing flood disaster mitigation in Indonesia through a qualitative descriptive approach using literature review methods. The findings show that AI can significantly contribute across all disaster management phases pre-disaster, emergency response, and post-disaster by supporting predictive modeling, real-time monitoring, decision support systems, and data-driven reconstruction strategies. The study adopts Decision Support System (DSS) theory by Gorry and Scott-Morton and Technological Determinism theory by McLuhan to analyze how AI not only improves operational capacity but also transforms institutional and policy frameworks for disaster management in Indonesia. The integration of AI into flood mitigation efforts is expected to build a more proactive, adaptive, and responsive disaster management system aligned with national and global disaster risk reduction frameworks.
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Antara. (2025). BNPB Catat 42 Bencana Hidrometeorologi dalam 24 Jam, Banjir dan Longsor Terus Melanda Wilayah Indonesia. Times Indonesia. Diakses pada 3 Juli 2025, dari: https://timesindonesia.co.id/peristiwa-nasional/540124/bnpb-catat-42-bencana-hidrometeorologi-dalam-24-jam-banjir-dan-longsor-terus-melanda-wilayah-indonesia
BNPB. (2025). Data Bencana Indonesia. Badan Nasional Penanggulangan Bencana. Diakses pada 29 Juni 2025, dari: https://gis.bnpb.go.id/databencana/tabel/pencarian.php
Cahyani, K. D., Sari, I. P., & Mulyono, S. (2025). Adaptasi Perubahan Iklim dan Kesiapsiagaan Bencana di Masyarakat Menggunakan Artificial Intelligence. Jurnal Penelitian Kesehatan" SUARA FORIKES"(Journal of Health Research" Forikes Voice"), 16(2), 571-576. Diakses pada 5 Juli 2025, dari: http://www.forikes-ejournal.com/index.php/SF/article/view/5865
Eriana, E. S., & Zein, A. (2023). Artificial Intelligence (AI). Diakses pada 3 Juli 2025, dari: https://repository.penerbiteureka.com/id/publications/567027/artificial-intelligence-ai
Fahrimal, Y., Reza, M., & Tjoetra, A. (2019). Revolusi Industri 4.0 Dalam Penguatan Kesiapsiagaan Bencana di Indonesia. Communication, 10(2), 128-141. Diakses pada 5 Juli 2025, dari: https://www.academia.edu/download/104637262/pdf_10.pdf
Gorry, G. A., & Morton, M. S. S. (1989). A framework for management information systems. MIT Sloan Management Review, 30(3), 49. Diakses pada 29 Juni 2025, dari: https://search.proquest.com/openview/1e52189526f99b7a5fcd55b4bc87cdff/1?pq-origsite=gscholar&cbl=26142
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2020). Processing Social Media Messages in Mass Emergency: A Survey. ACM Computing Surveys (CSUR), 52(6), 1–38. Diakses pada 6 Juli 2025, dari: https://doi.org/10.1145/3295748
Krismantoro, T., Suhendi, A., & Saputra, C. (2023). Analisis Penerapan ANN dan RNN dengan Inisialisasi Nguyen-Widrow pada Aplikasi Monitoring Banjir dan Gempa. eProceedings of Engineering, 10(5). Diakses pada 6 Juli 2025, dari: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/download/21274/20549
McLuhan, M. (1994). Understanding media: The extensions of man. MIT press. Diakses pada 29 Juni 2025, dari: https://scholar.google.com/scholar?cluster=4603169387763961023&hl=en&oi=scholarr
Nursyabani, N., Putera, R. E., & Kusdarini, K. (2020). Mitigasi bencana dalam peningkatan kewaspadaan terhadap ancaman gempa bumi di universitas andalas. Jurnal Ilmu Administrasi Negara ASIAN (Asosiasi Ilmuwan Administrasi Negara), 8(2), 81-90. Diakses pada 6 Juli 2025, dari: http://jurnal.asian.or.id/index.php/JIANA/article/view/12
Rahman, M. M., Ullah, A. M. M. S., & Rahman, M. M. (2022). AI-Driven Disaster Logistics Optimization: A Systematic Review. International Journal of Disaster Risk Reduction, 71. Diakses pada 6 Juli 2025, dari: https://doi.org/10.1016/j.ijdrr.2022.102816
Republik Indonesia. (2007). Undang-Undang Republik Indonesia Nomor 24 Tahun 2007 tentang Penanggulangan Bencana. Diakses pada 30 Juni 2025, dari: https://bnpb.go.id/ppid/file/UU_24_2007.pdf
Riza, H., Santoso, E., Tejakusuma, I., & Prawiradisastra, F. (2023). Pemanfaatan kecerdasan artifisial untuk meningkatkan mitigasi bencana banjir. Diakses pada 5 Juli 2025, dari: https://www.researchgate.net/profile/Firman-Prawiradisastra/publication/369842927_Pemanfaatan_Kecerdasan_Artifisial_untuk_Meningkatkan_Mitigasi_Bencana_Banjir/links/64b0f22fb9ed6874a5185a93/Pemanfaatan-Kecerdasan-Artifisial-untuk-Meningkatkan-Mitigasi-Bencana-Banjir.pdf
Riza, H., Santoso, E. W., & Tejakusuma, I. G. (2020). Utilization Of Artificial Intelligence to Improve Flood Disaster Mitigation Pemanfaatan Kecerdasan Artifisial Untuk Meningkatkan Mitigasi Bencana Banjir. Jurnal Sains dan Teknologi Mitigasi Bencana, 15(1). Diakses pada 5 Juli 2025, dari: https://www.researchgate.net/profile/Firman-Prawiradisastra/publication/342597201_UTILIZATION_OF_ARTIFICIAL_INTELLIGENCE_TO_IMPROVE_FLOOD_DISASTER_MITIGATION/links/613692a9c69a4e4879843631/UTILIZATION-OF-ARTIFICIAL-INTELLIGENCE-TO-IMPROVE-FLOOD-DISASTER-MITIGATION.pdf?origin=journalDetail&_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9
Rizti, F. (2024). Terkini! Perbandingan Jumlah Bencana Alam di Indonesia 2020-2024. GoodStats. Diakses pada 3 Juli 2024, dari: https://data.goodstats.id/statistic/terkini-perbandingan-jumlah-bencana-alam-di-indonesia-2020-2024-lWikM
Sakurai, M., & Murayama, Y. (2019). Information technologies and disaster management–Benefits and issues. Progress in Disaster Science, 2, 100012. Diakses pada 6 Juli 2025, dari: https://www.sciencedirect.com/science/article/pii/S2590061719300122
Sugiyono. (2023). Metode Penelitian Kualitatif. Bandung: Alfabeta.
Suri, H., & Narayanamurthy, G. (2019). Enhancing Situational Awareness in Disaster Response Using AI. Procedia Computer Science, 152, 110–117. Diakses pada 6 Juli 2025, dari: https://doi.org/10.1016/j.procs.2019.05.010
UN Global Pulse. (2022). Using Real-Time Social Media Data for Humanitarian Response in Indonesia: Pulse Lab Jakarta Findings. Diakses pada 6 Juli 2025, dari: https://www.unglobalpulse.org
Urbanus, A., Sela, R. L., & Tungka, A. E. (2021). Mitigasi bencana banjir struktural dan non-struktural di Kabupaten Bolaang Mongondow Selatan. Spasial, 8(3), 447-458. Diakses pada 6 Juli 2025, dari: https://ejournal.unsrat.ac.id/index.php/spasial/article/view/36350