An Iot-Based Flood Early Warning System Integrated with the Sesaga Approach - Rachmat Kasim; Eric Alfonsius (Jurnal Teknoinfo, 2026)
Floods are among the natural disasters that significantly impact human safety, infrastructure, and the economy, particularly in flood-prone areas. Therefore, an early detection system capable of providing fast, accurate, and adaptive information is required to support risk mitigation efforts. This study develops a Flood Early Detection System based on the SESaGa approach (Systematic, Exploratory, SWOT, and GIS Analysis), which integrates Systematic Literature Review (SLR), Exploratory Data Analysis (EDA), SWOT, and GIS. Research data were obtained from a flood monitoring prototype installed at several flood-prone points in Gorontalo. The implementation results show that the SESaGa approach enhances early detection accuracy and provides valuable spatial information for mitigation planning. In addition, the integration of SWOT analysis generates strategic recommendations for strengthening the system and disaster management policies. The developed system was also tested using the blackbox testing method to ensure that the software functionalities meet user requirements. The test results demonstrate that all key features, including sensor data monitoring, warning notifications, and GIS-based visualization, functioned properly without critical errors. Thus, this system has the potential to serve as an essential instrument in supporting community resilience against flood threats and improving the effectiveness of mitigation measures in vulnerable regions.