Poverty Level Prediction Based on E-Commerce Data Using K-Nearest Neighbor and Information-Theoretical-Based Feature Selection

Abstract

The Central Statistics Agency (BPS) is a government agency that focuses on matters concerning household economic and social needs. Every two years BPS conducts Susenas (National Socio-Economic Survey) to measure poverty levels in Indonesia. Every year BPS is tasked with providing information about the community's welfare in terms of their socio-economic aspects. In this very rapid development, many methods can be used to determine the poverty level. One of them is with the use of the rapid development of E-commerce in Indonesia, which can determine the level of poverty in Indonesia. In this study, we proposed a method to predict the poverty level based on an e-commerce dataset using K-Nearest Neighbor and Information Theoretical Based Feature Selection. Our method is expected to be able to complement the BPS Census and Susenas in predicting poverty levels in an area. Our test results show that our method data can predict the poverty level although there are rooms for improvement in terms of accuracy.