Risk Identification and Disaster Management at The Village Level: Principal Component Analysis Approach

Abstract

Indonesia is one of the countries with a fairly high level of disaster proneness. Based on the results of the 2020 Indonesia Disaster Risk Index (IRBI) published by BNPB, out of the number of 514 districts, there are 237 districts with high risk, while 277 districts with moderate risk. The high number of Indonesian disasters can also be seen from the number of disaster events. So far, disaster identification is limited to the district. The disaster risk index also has an area only up to the district. Whereas each village has different location characteristics so that disaster management cannot be equated. Therefore, this study tried to look at the risk of disaster-prone at the village level. The data used is the 2020 Village Potential data by looking at the number of disaster events and also the number of fatalities in each village from 2019 to March 2020. The method used an analysis description approach through data exploration. In addition, using quantitative methods principal analysis components to create an Index that will classify a village whether prone to disaster or not. The results of identification are still many villages that are prone to disaster. From these results, it is mapped that there are 1,158 villages that have high risk, 27,061 medium risk and 46,446 villages are in low risk. This means that about 38 thousand still have a risk of being prone to disasters.