Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction

Abstract

The increasing threat of natural disasters in Yogyakarta, Indonesia, calls for technological solutions to predict and monitor disaster events more accurately. The development of the DisasterWatch application aims to provide a reliable disaster prediction tool, using machine learning and data mining-based predictive modeling methods. The app utilizes various machine learning algorithms to analyze historical disaster data as well as related risk factors, such as weather, seismic activity and soil conditions. Preliminary results show that this prediction system can improve the accuracy of early warnings, but further development is needed to ensure its accessibility and reliability for the community. In addition, to ensure its accessibility and reliability for the wider community, especially in disaster-prone areas. Collaboration with BMKG, and local communities is being sought to ensure this application can be practically implemented in the field