Water Level Classification for Detect Flood Disaster Status Using KNN and SVM
| Autor: | Jiwa Akbar, Muchtar Ali Setyo Yudono |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Jurnal Sisfokom, Vol 13, Iss 3, Pp 298-302 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2301-7988 2581-0588 |
| DOI: | 10.32736/sisfokom.v13i3.2166 |
| Popis: | Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10. |
| Databáze: | Directory of Open Access Journals |
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