Classification of Poverty Levels Using k-Nearest Neighbor and Learning Vector Quantization Methods

Abstract

Poverty is the inability of individuals to fulfill the minimum basic needs for a decent life. The problem of poverty is one of the fundamental problems that become the central attention of the local government. One of the government efforts to overcome poverty is using the alleviation programs. Government often faces some difficulties to sort out of the poverty levels in the society. Therefore it is necessary to conduct a study that helps the government to identify the poverty level so that the aid did not miss the targets. In order to tackle this problem, this paper leverages two classification methods: k-nearest neighbor (k-NN) and learning vector quantization (LVQ). The purpose of this study is to compare the accuracy of the value of both methods for classifying poverty levels. The data attributes that are used to characterize poverty among others include: aspects of housing, health, education, economics and income. From the testing results using both methods, the accuracy of k-NN is 93.52%, and the accuracy of LVQ is 75.93%. It can be concluded that the classification of poverty levels using k-NN method gives better performance than using LVQ method.