Floods are natural disasters that frequently occur in Indonesia, including Central Kalimantan, causing significant impacts on communities, infrastructure, and economic activities. One of the primary factors contributing to delayed flood response is the absence of an effective and real-time early warning system. This study aims to design and develop an Internet of Things (IoT)-based flood monitoring and early warning system capable of monitoring water levels in real time and delivering alert notifications to the public via WhatsApp. The system employs an A01NYUB ultrasonic sensor to measure water elevation and a rain gauge sensor to measure rainfall, with ESP32 and SIM7600CE modules providing internet connectivity through Wi-Fi and cellular networks, and an LC86 Quectel module enabling location tracking. Power is supplied by solar panels and batteries to ensure autonomous operation in remote areas. The early warning mechanism is implemented using the Mamdani Fuzzy Inference System, which processes water elevation, rate of water level change, and rainfall intensity to calculate flood risk percentages in real time. Automatic alerts are issued when the flood risk exceeds a 75% threshold. The system development follows the ADDIE methodology. The results are expected to support faster and more efficient flood mitigation efforts.
