{"title":"Fuzzy Time Series Lee dengan Average Based Length untuk Prediksi Jumlah Penduduk Miskin Sulawesi Tenggara","authors":"Fithriah Musadat, Jabal Nur, Andi Nasri","doi":"10.55340/japm.v9i1.1270","DOIUrl":null,"url":null,"abstract":"This study aims to predict the number of poor people in Southeast Sulawesi using the Lee’s Fuzzy Time Series with Average Based Length. The data used in this research is annual periodic data from 2004-2022 downloaded from the bps.go.id website. The results of the study show that the poverty rate in Southeast Sulawesi in 2023 will reach 301,801 people. The model is successful in reading data movements, with the number of error values > 0 indicating the direction of prediction error which tends to be underestimated. However, the resulting accuracy is very good, indicated by the MAPE value of 1.71%.","PeriodicalId":399332,"journal":{"name":"Jurnal Akademik Pendidikan Matematika","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Akademik Pendidikan Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55340/japm.v9i1.1270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Time Series Lee dengan Average Based Length untuk Prediksi Jumlah Penduduk Miskin Sulawesi Tenggara
This study aims to predict the number of poor people in Southeast Sulawesi using the Lee’s Fuzzy Time Series with Average Based Length. The data used in this research is annual periodic data from 2004-2022 downloaded from the bps.go.id website. The results of the study show that the poverty rate in Southeast Sulawesi in 2023 will reach 301,801 people. The model is successful in reading data movements, with the number of error values > 0 indicating the direction of prediction error which tends to be underestimated. However, the resulting accuracy is very good, indicated by the MAPE value of 1.71%.
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