Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan

Abstract

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results