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dc.contributor.advisorSyahputra, Muhammad Romi
dc.contributor.authorBerutu, Sabella Allende
dc.date.accessioned2024-09-02T08:36:34Z
dc.date.available2024-09-02T08:36:34Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/96551
dc.description.abstractPoverty is defined as a state in which a person is unable to meet their basic needs such as food, clothing, shelter, and education. The purpose of this study is to group all districts and cities in Central Java Province into several clusters based on the similarity of the characteristics of social assistance recipients. The method used in this study is a non-hierarchical method, namely the k-means clustering method. The variables used in this study are the elderly, mental disabilities, poor families, victims of natural disasters. Based on the results of the research, four clusters were obtained, namely: Cluster 1 consists of 4 regencies/cities with high social assistance recipients, namely: Blora Regency, Kendal Regency, Batang Regency, Pemalang Regency. Cluster 2 consists of 8 regencies/cities with moderate social assistance recipients, namely: Cilacap, Banyumas, Purbalingga, Magelang, Klaten, Rembang, Demak, Temanggung Regencies.Cluster 3 consists of 3 regencies/cities with low social assistance recipients, namely: Kebumen, Wonogiri, Tegal Regencies.Cluster 4 consists of 16 regencies/cities with very low social assistance recipients, namely: Banjarnegara, Purwokerto, Wonosobo, Boyolali, Karanganyar, Sragen, Grobongan, Pati, Kudus, Semarang, Pekalongan, Brebes, Surakarta, Salatiga, Semarang City, Tegal. Then a significance test was carried out with the ANOVA test, it was known that the X3 variable had a significance value of 0.000 < 0.05, then the X3 variable had the greatest difference between the X1, X2, and X4 variables.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectPovertyen_US
dc.subjectSocial Assistanceen_US
dc.subjectK-Meansen_US
dc.subjectANOVAen_US
dc.subjectSDGsen_US
dc.titlePenerapan Algoritma K-Means pada Pengelompokan Kabupaten/Kota Berdasarkan Indikator Penerima Bantuan Sosial di Provinsi Jawa Tengahen_US
dc.title.alternativeApplication of The K-Means Algorithm in Clustering Districts / Cities Based on Indicators of Social Assistance Recipients in Central Java Provinceen_US
dc.typeThesisen_US
dc.identifier.nimNIM212407040
dc.identifier.nidnNIDN0115118903
dc.identifier.kodeprodiKODEPRODI49401#Statistika
dc.description.pages52 Pagesen_US
dc.description.typeKertas Karya Diplomaen_US


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