{"title":"Clustering of Districts in Indonesia using the 2015 High School Social Sciences National Examination Results","authors":"R. Ferdhiana, K. Amri, T. Abidin","doi":"10.1109/ICICoS48119.2019.8982524","DOIUrl":null,"url":null,"abstract":"This study aims to cluster 513 districts in Indonesia using the results of High School National Examination or “Ujian Nasional (UN)” in Indonesian language majoring in social sciences to map the learning outcomes in the districts. The attributes consist of 6 subjects which are Bahasa Indonesia, English, Mathematics, Economics, Sociology, and Geography. The clustering methods used are Complete-linkage and K-Means. The clustering results are compared with the District Human Development Index (HDI) of the clusters. The results show that the districts in Indonesia are grouped into 5 clusters and there is a slight dissimilarity between the scores of UN and HDI.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICoS48119.2019.8982524","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 study aims to cluster 513 districts in Indonesia using the results of High School National Examination or “Ujian Nasional (UN)” in Indonesian language majoring in social sciences to map the learning outcomes in the districts. The attributes consist of 6 subjects which are Bahasa Indonesia, English, Mathematics, Economics, Sociology, and Geography. The clustering methods used are Complete-linkage and K-Means. The clustering results are compared with the District Human Development Index (HDI) of the clusters. The results show that the districts in Indonesia are grouped into 5 clusters and there is a slight dissimilarity between the scores of UN and HDI.
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