Village Based Disaster Risk Identification: Principal Component Analysis Approach
Muhammad Fazri (a*), Chalin Antinia Agustin (a)
a) The Agencys Research and Development, Education and Training, and Information, Ministry of Village, Development of Disadvantaged Regions and Transmigration, Jakarta,12750, Indonesia
*fazri.ie[at]gmail.com
Abstract
Indonesia is one of the countries with a high level of disaster risk. Based on the results of the 2013 Indonesian Disaster Risk Index (IRBI) published by BNPB there are 388 districts with high risk, while 109 districts have moderate risks. High level disaster of Indonesia can also be seen from the number of disasters. The results of BNPB mentioned 12.320 disasters occurred in Indonesia during 2011-2016. Recently, disaster identification is only limited to the district. The disaster risk index also has areas only to districts. Whereas in each village has different location characteristics so that disaster management cannot be equated. Therefore, this study attempts to look at the risk of disaster-prone at the village level. The data used is the 2018 Podes data by looking at the number of disaster events and also the number of fatalities in each village from 2015-2017. The method used uses an approach to analyze analysis through data exploration. In addition, it uses the quantitative method of the principal component of analysis to create an index that will classify a village as prone to disasters or not. The results of the identification show the number of villages experiencing disasters during 2015-2017 for landslides of 9.296; Flood 17.299; Banjir Bandang 1.670; Earthquake 9.247; Tsunami 12 Villages; Sea Tide Wave 1.587; Whirlwind 6.259; Vulcanic Eruption 585; Forest / Land Fires 4.067 and Droughts 8.214 Village. Whereas those with early warning are only around 6716 villages.
Keywords: Disaster, Principal Component Analysis, Village
Topic: Disaster Management