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Journal > International Journal of Computing Science and Applied Mathematics > Classification of Poverty Level Using k-Nearest Neighbor and Learning Vector Quantization Methods

 

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International Journal of Computing Science and Applied Mathematics
Vol 2, No 1 (2016): March
Classification of Poverty Level Using k-Nearest Neighbor and Learning Vector Quantization Methods
Article Info   ABSTRACT
Published date:
01 Apr 2016
 
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 of attention of local government. The government efforts to overcome poverty through with alleviation programs. Government is often difficult to sort out of the poverty levels in the society, therefore is necessary to a study that may help the government to identify of the poverty level in order that aid the government did not missed targeted. The aim is featured two classification methods of k-NN and LVQ. The purpose of this study was to compare the accuracy of the value of both methods for classification results in poverty levels. The data is used as an attribute that is data that characterizes poverty among others include aspects of housing, health, education, economics and income. From the testing results using both methods shows an accuracy value uses k-NN is 93.52 %, and an accuracy value uses LVQ is 75.93 %. It can be concluded that the classification of poverty level using k-NN method gives better performance than Learning Vector Quantization.
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