Predicting Flood Risk in Manado City Using the C4.5 Decision Tree Algorithm: A Data-Driven Approach - Apriandy Angdresey; Indah Yessi Kairupan; Ranodeyansa Rumanjar; Andre Gabriel Mongkareng; Ignatius Lucky Henokh Tangka (EPI International Journal of Engineering, 2025)
Flooding, a natural disaster commonly triggered by heavy rainfall or blocked waterways, poses a persistent threat to Manado City due to its proximity to the sea and major rivers, Tondano and Tikala. This study develops a flood prediction application using the C4.5 Decision Tree algorithm based on historical data (2017–2021) from five rivers. Input variables include rainfall, water discharge, runoff coefficients, and river cross-sectional data. The model supports the Sulawesi River Regional Office I by predicting flood conditions—flood, prone to flooding, or no flooding—to enhance mitigation strategies. Experimental results show robust predictive performance, achieving accuracies of 86.13%-87.07% across different data splits. Future integration with the Internet of Things (IoT) is proposed to enable real-time data acquisition, thereby improving the system's responsiveness and flood risk management effectiveness.