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dc.contributor.advisorSutarman
dc.contributor.authorSitungkir, Marta A
dc.date.accessioned2024-01-15T07:17:42Z
dc.date.available2024-01-15T07:17:42Z
dc.date.issued2023
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/90194
dc.description.abstractCluster analysis is a technique used to show the distance or similarity relationship between objects and variables. There are two methods in this analysis, namely hierarchical methods and non-hierarchical methods. The hierarchical method includes several methods such as complete linkage, average linkage, single linkage, and ward linkage. In this study, the hierarchical method using the average linkage method was used to group districts/cities in North Sumatra Province based on their welfare levels. In the results of this study, four clusters were formed from 33 districts/municipalities in North Sumatra Province. Cluster 1 consists of the districts of Nias, South Nias, West Nias, and North Nias. Cluster 2 consists of the districts of Mandailing Natal, South Tapanuli, Central Tapanuli, North Tapanuli, Toba Samosir, Labuhan Batu, Asahan, Simalungun, Dairi, Karo, Langkat, Humbang Hasundutan, Pakpak Bharat, Samosir, Serdang Bedagai, Batu Bara, North Padang Lawas, Padang Lawas, South Labuhanbatu, North Labuanbatu, Sibolga, Tanjungbalai, Pematangsiantar, Tebing Tinggi, Medan, Binjai, Padangsidimpuan, and Gunungsitoli. Cluster 3 consists only of Deli Serdang Regency, while Medan City is the only member included in cluster 4.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectAverage Linkageen_US
dc.subjectclusteren_US
dc.subjectHierarchyen_US
dc.subjectWelfare Indicatorsen_US
dc.subjectSDGsen_US
dc.titleAnalisis Cluster untuk Mengelompokkan Kabupaten/Kota Berdasarkan Indikator Kesejahteraan Rakyat di Wilayah Provinsi Sumatera Utaraen_US
dc.typeThesisen_US
dc.identifier.nimNIM202407045
dc.identifier.nidnNIDN0026106305
dc.identifier.kodeprodiKODEPRODI49401#Statistika
dc.description.pages67 Halamanen_US
dc.description.typeKertas Karya Diplomaen_US


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