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dc.contributor.advisorTarigan, Gim
dc.contributor.advisorSitepu, Henry Rani
dc.contributor.authorReza, Widya
dc.date.accessioned2022-12-23T02:18:25Z
dc.date.available2022-12-23T02:18:25Z
dc.date.issued2016
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/76979
dc.description.abstractPoverty indicators are compiled to determine a household is poor or not turns out to be reaping some of the critism about mistakes in goal setting program of poverty reduction. This is caused due to a difference in the characteristics of poor household in each region. For that should be noted the characteristics of poor household in North Sumatera and done characteristics based of clustering. Cluster analysis is a class of techniques that are used for grouping objects into relatively homogeneous groups called clusters. Based on the result of cluster analysis agains a grouping of regency/ city in North Sumatera. Based on the characteristics of poor households obtained five regional groups with different characteristics. Cluster I consist of Nias, Nias Selatan, Nias Utara, dan Nias Barat. Cluster II consist of Mandailing Natal, Padang Lawas dan Kota Gunung Sitoli. Cluster III consist of Tapanuli Utara, Dairi, Humbang Hasundutan and Samosir. Cluster IV consist of Tapanuli Selatan, Tapanuli Tengah, Tapanuli Utara, Dairi, Humbang Hasundutan, Pakpak Bharat, Samosir dan Padang Lawas Utara. Cluster V consist of Deli Serdang, Kota Sibolga, Tanjung Balai, Pematang Siantar, Tebing Tinggi, Medan, Binjai dan Padang Sidempuan.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectPovertyen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectClusteren_US
dc.titlePengelompokan Kabupaten/ Kota di Sumatera Utara Berdasarkan Karakteristik Rumah Tangga Miskin dengan Menggunakan Analisis Clusteren_US
dc.typeThesisen_US
dc.identifier.nimNIM140823016
dc.identifier.nidnNIDN0002025505
dc.identifier.nidnNIDN0003035305
dc.identifier.kodeprodiKODEPRODI442013#Matematika
dc.description.pages73 Halamanen_US
dc.description.typeSkripsi Sarjanaen_US
dc.description.typeTesis Magisteren_US


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