Studi Literatur Pemanfaatan Artificial Intelligence untuk Prediksi Bencana Banjir
Authors
-
Denada Fatimah Zahra
AMIK YPAT Purwakarta
-
Carkiman Carkiman
Universitas Mandiri
https://doi.org/10.47561/jtik.v18i1.281
This Abstract has been read 772 times
Abstract
Floods are disasters that cause losses in the form of material and human casualties if not controlled. The Bekasi flood in 2025 was a flood event that was proven to paralyze economic activities and inundate crucial economic zones and settlements in the Bekasi area. Basically, floods can be categorized as fluvial, pluvial, rob, and flash. There are various factors that cause floods that cause diversity in types and levels of flood destruction. Therefore, previous studies have attempted to predict floods from a physical perspective, but still experience various obstacles, so this study aims to explain the use of artificial intelligence for flood disaster prediction. The research method used is a literature study in order to represent the use of artificial intelligence in various areas and techniques. The data used is in the form of scientific literature consisting of journals and scientific proceedings. The results of the study show that most of the AI used is based on machine learning algorithms, such as random forest, logistic regression, support vector machine, and KNN. Random forest and logistic regression have proven to have the highest performance and accuracy when compared to other algorithms. However, it needs to be combined with data augmentation techniques and consider time when used for real-time prediction needs.
Keywords: Artificial Intelligence, Flood, Prediction
CITATIONS
PDF Downloads
References
Prihatini, W. (2025, March 4). No. Tirto.Id. https://tirto.id/titik-banjir-bekasi-hari-ini-4-maret-2025-
info-daerah-terendam-g8ZQ
Kundzewicz, Z. W., & Pi?skwar, I. (2022). Are Pluvial and Fluvial Floods on the Rise? Water, 14(17),
https://doi.org/10.3390/w14172612
Xu, L., Yang, X., Cui, S., Tang, J., Ding, S., & Zhang, X. (2023). Co-occurrence of pluvial and fluvial
floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in
Longyan, China.Geomatics,Natural Hazards and Risk,14(1).
https://doi.org/10.1080/19475705.2023.2218012
Yonehara, S., & Kawasaki, A. (2020). Assessment of the tidal effect on flood inundation in a low?lying
river basin under composite future scenarios. Journal of Flood Risk Management, 13(3).
https://doi.org/10.1111/jfr3.12606
Zhang, W., Luo, M., Gao, S., Chen, W., Hari, V., & Khouakhi, A. (2021). Compound
Hydrometeorological Extremes: Drivers, Mechanisms and Methods. Frontiers in Earth Science, 9.
https://doi.org/10.3389/feart.2021.673495
Feng, B., Zhang, Y., & Bourke, R. (2021). Urbanization impacts on flood risks based on urban growth
data and coupled flood models. Natural Hazards, 106(1), 613–627. https://doi.org/10.1007/s11069-020-
-0
Yan, X., Liu, J., Rühland, K. M., Dong, H., He, J., & Smol, J. P. (2022). Human deforestation
outweighed climate as factors affecting Yellow River floods and erosion on the Chinese Loess Plateau since
the
th
century.
Quaternary
Science
Reviews,
,
https://doi.org/10.1016/j.quascirev.2022.107796
Elvira, Neni Z, D. (2022). Studi Literatur: Motivasi Belajar Siswa dalam Pembelajaran. Jurnal Literasi
Pendidikan,1(2),350–359.
https://journal.citradharma.org/index.php/eductum/indexDOI:https://doi.org/10.56480/eductum.v1i2.76
%0Ahttps://creativecommons.org/licenses/by-sa/4.0/
Tuginem, H. N. (2023). PENELITIAN STRATEGI PENGEMBANGAN KOLEKSI DI
PERPUSTAKAAN PADA GOOGLE SCHOLAR: SEBUAH NARRATIVE LITERATURE REVIEW.
Jurnal Pustaka Budaya, 10(1), 32–43. https://doi.org/10.31849/pb.v10i1.11275
Helen Joyice, M., Valli Sri Vidya, K., Vijaya Lakshmi, L., Juilath, M., Prajwala, K., & Srinu Vasarao,
P. (2024). Identifying Flood Prediction using Machine Learning Techniques. International Journal of
Innovative
Science
and
Research
Technology
(IJISRT),
–147.
https://doi.org/10.38124/ijisrt/IJISRT24MAR112
Hadi, F. A. A., Mohd Sidek, L., Ahmed Salih, G. H., Basri, H., Sammen, S. S., Mohd Dom, N.,
Muhamad Ali, Z., & Najah Ahmed, A. (2024). Machine learning techniques for flood forecasting.
Journal of Hydroinformatics, 26(4), 779–799. https://doi.org/10.2166/hydro.2024.208
Dtissibe, F. Y., Ari, A. A. A., Titouna, C., Thiare, O., & Gueroui, A. M. (2020). Flood forecasting
based on an artificial neural network scheme. Natural Hazards, 104(2), 1211–1237.
https://doi.org/10.1007/s11069-020-04211-5
Kim, H. Il, & Han, K. Y. (2020). Urban Flood Prediction Using Deep Neural Network with Data
Augmentation. Water, 12(3), 899. https://doi.org/10.3390/w12030899
Hofmann, J., & Schüttrumpf, H. (2021). floodGAN: Using Deep Adversarial Learning to Predict
Pluvial Flooding in Real Time. Water, 13(16), 2255. https://doi.org/10.3390/w13162255
Arya Darmawan, M. B., Dewanta, F., & Astuti, S. (2023). Analisis Perbandingan Algoritma Decision
Tree, Random Forest, dan Naïve Bayes untuk Prediksi Banjir di Desa Dayeuhkolot. TELKA -Telekomunikasi Elektronika Komputasi Dan Kontrol,9(1),52–61.
https://doi.org/10.15575/telka.v9n1.52-61
Hermawan, E. et al. (2024). Sistem Prediksi Banjir Rob Kota Pontianak Berbasis Machine Learning
Menggunakan Framework Streamlit. Jurnal Edukasi & Penelitian Informatika, 10(3).
Dwiasnati, S. et al. (2021). Optimasi Prediksi Bencana Banjir menggunakan Algoritma SVM untuk
penentuan
Daerah
Rawan
Bencana
Banjir.
Prosiding
SISFOTEK.
http://www.seminar.iaii.or.id/index.php/SISFOTEK/article/view/283
Sandiwarno, S. (2024). Penerapan Machine Learning Untuk Prediksi Bencana Banjir. Jurnal Sistem
Informasi Bisnis, 14(1), 62–76. https://doi.org/10.21456/vol14iss1pp62-76
RACHMAWARDANI, A., WIJAYA, S. K., PRAWITO, P., & SOPAHELUWAKAN, A. (2024).
Prediksi Banjir menggunakan ANFIS-PCA sebagai Peringatan Dini Bencana Banjir. ELKOMIKA:
Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 12(2), 335.
