Suyanto, Wiwik Budiarti, R. E. Standsyah, D. Ramadhan
{"title":"Efforts to Alleviate Underdeveloped Areas by Clustering Regional Characteristics in Indonesia","authors":"Suyanto, Wiwik Budiarti, R. E. Standsyah, D. Ramadhan","doi":"10.55927/ministal.v2i4.5531","DOIUrl":null,"url":null,"abstract":"This study aims to cluster the underdeveloped regions in Indonesia according to the criteria of the underdeveloped indicator to mitigate the underdeveloped regions in Indonesia. This research was conducted to help the various efforts made by the government to deal with the underdeveloped regions, by grouping the underdeveloped regions, it is hoped that the government can focus on increasing the dominant criteria in the regions according to the cluster. The grouping method used is K-means with the results of 62 underdeveloped districts in Indonesia divided into 3 clusters. The first cluster includes 23 districts grouped based on human resource criteria, the second cluster consists of 28 districts based on infrastructure/facilities criteria, and the third cluster consists of 11 districts based on economics criteria.","PeriodicalId":477145,"journal":{"name":"Jurnal Ekonomi dan Bisnis Digital","volume":"56 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Ekonomi dan Bisnis Digital","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.55927/ministal.v2i4.5531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims to cluster the underdeveloped regions in Indonesia according to the criteria of the underdeveloped indicator to mitigate the underdeveloped regions in Indonesia. This research was conducted to help the various efforts made by the government to deal with the underdeveloped regions, by grouping the underdeveloped regions, it is hoped that the government can focus on increasing the dominant criteria in the regions according to the cluster. The grouping method used is K-means with the results of 62 underdeveloped districts in Indonesia divided into 3 clusters. The first cluster includes 23 districts grouped based on human resource criteria, the second cluster consists of 28 districts based on infrastructure/facilities criteria, and the third cluster consists of 11 districts based on economics criteria.