{"title":"PENERAPAN LOGIKA FUZZY UNTUK PENYALURAN RASKIN BAGI MASYARAKAT KURANG MAMPU DI KECAMATAN SAGULUNG","authors":"Yusli Yenni, Nia Diana","doi":"10.22202/EI.2018.V4I2.2942","DOIUrl":null,"url":null,"abstract":"This research aims to implement fuzzy logic to determine acceptance Beras Miskin and describe the level of accuracy. implement some of the possibilities in the selection of poor rice Admission for people less able to match. In this study, to analyze Logiak fuzzy using MATLAB software Help. There are 7 information used as input fuzzy. Input fuzzy model using triangular and trapezoidal membership functions to construct fuzzy rules on the 87 data, so there are 54 fuzzy rules. Having obtained fuzzy rules of inference process is then performed and defuzzification. Inference is the method mamdani. Results defuzzification is the value for Determining Acceptance Beras Miskin divided into two categories: Receiving and Not Receiving. Fuzzy model that has been built will be testing the model by determining the level of accuracy and error of the model. With the results of 95.4% with an error of 4.6%.","PeriodicalId":300901,"journal":{"name":"Edik Informatika","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Edik Informatika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22202/EI.2018.V4I2.2942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This research aims to implement fuzzy logic to determine acceptance Beras Miskin and describe the level of accuracy. implement some of the possibilities in the selection of poor rice Admission for people less able to match. In this study, to analyze Logiak fuzzy using MATLAB software Help. There are 7 information used as input fuzzy. Input fuzzy model using triangular and trapezoidal membership functions to construct fuzzy rules on the 87 data, so there are 54 fuzzy rules. Having obtained fuzzy rules of inference process is then performed and defuzzification. Inference is the method mamdani. Results defuzzification is the value for Determining Acceptance Beras Miskin divided into two categories: Receiving and Not Receiving. Fuzzy model that has been built will be testing the model by determining the level of accuracy and error of the model. With the results of 95.4% with an error of 4.6%.