Water Level Classification for Early Flood Detection Using KNN Method - Akbar, Jiwa; Yudono, Muchtar Ali Setyo (Universitas Nusa Putra, 2024)
Floods occur when water levels exceed normal limits, causing rivers to overflow and inundate low-lying areas. Early warning systems for flood disasters are crucial to mitigate the damage caused, such as loss of life and property. A flood classification system can be developed by utilizing water level data from the Department of Water Resources to predict the likelihood of flooding using the K-Nearest Neighbors (KNN) algorithm. This study aims to determine the flood status classification based on water levels using the KNN method in the Ciliwung River. The research data were obtained from the DKI Jakarta open data site, consisting of 564 samples. The study evaluated K values ranging from 1 to 10. The average accuracy across all K scenarios was 99%, with the best K value being 1, which provided 100% accuracy, sensitivity, and specificity. These results indicate that the KNN method is effective in classifying flood status based on water level data, making it a reliable tool for early warning systems. This system is expected to help reduce the negative impacts of floods by providing accurate and timely information to the public and authorities. This research makes a significant contribution to the development of disaster mitigation technology, particularly in flood risk management in urban areas.