A QGIS-based Information System for the Coastal Vulnerability Classification with ANN Method

The coastal environment is very vulnerable to various natural disasters, especially if the coastal environment has been damaged, resulting in a reduction in the environmental carrying capacity. Disasters that generally occur in Indonesia's coastal areas include coastal erosion, tsunamis, floods, rising sea levels, droughts, and landslides. Disasters in coastal areas often cause large losses of life and material. Therefore, it is important to measure the vulnerability of disasters in coastal areas, so that early warning can be given to communities who may be affected by disasters. This research aims to implement a QGIS-based information system that utilizes machine learning techniques to classify disaster vulnerability in coastal areas. This study demonstrates how the ANN (Artificial Neural Network) algorithm can be used to classify the level of vulnerability of the coastal areas. This study uses 5782 points along the coast of the Special Region of Y ogyakarta Province. The ANN method was utilized to classify coastal vulnerability, yielding a remarkable accuracy rate of 97.9• The resulting system provides a visual representation of the level of coastal vulnerability that is comprehensive, interactive, and understandable for the government, policymakers, and the general public. © 2024 IEEE.