Application of Random Forest to Identify for Poor Households in West Sumatera Province

Abstract

Poverty is a socioeconomic problem in Indonesia. The number of people who were living in poverty in West Sumatera increases for 26.44 thousands from 2020 to 2021. The government has created programs to cope with poverty by taking into account the criteria for the poor households. These criteria have been developed by using the data obtained through The National Socioeconomic Survey (Susenas). However, instead of.showing the actual location of poor household, the existing data only interprets the number of poor household. Thus make the program less effective. This could be overcome by classification analysis of random forest (RF). RF is collection of many decision trees. Before fitting RF, one has to determine the values if three tuning parameters, mtry, ntree and node size. The result are the smallest  OOB’s error rate (%) and Variable Importance Measure(VIM). The classification by RF in this research results in OOB’s error rate was 5.65% or accuracy rate was 94.35%  with tuning parameter using mtry=5 and ntree=500. Based on the VIM, the poor household’s criteria include sources of drinking water such as protected or unprotected spring water and surface water, lighting tools such as non-PLN electricity or no usage of electricity, fuel for cooking such as charcoal and firewood, and the head of the household being self-employed, a family worker, or unpaid with at least a junior high degree.