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dc.contributor.advisorSutarman
dc.contributor.authorSafina, Dela
dc.date.accessioned2024-08-28T03:57:02Z
dc.date.available2024-08-28T03:57:02Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/96237
dc.description.abstractThe availability of adequate and equitable health facilities is one of the important indicators in assessing the quality of the health system in a region. This study aims to cluster districts/cities in West Java based on the availability of health facilities using K-Means cluster analysis. Secondary data on health facilities in 2020-2021 from the Central Bureau of Statistics was used with variables on the number of hospitals, maternity hospitals, polyclinics, health centers, auxiliary health centers, and pharmacies in 27 districts/cities. After descriptive analysis, the clustering process is carried out by determining the number of clusters, the initial centroid point, calculating the distance of each data to the centroid, grouping the data into the nearest cluster, and updating the centroid until the condition converges. The results showed that three optimal and stable clusters were formed after two iterations. The first cluster consists of 5 districts/cities with high availability of health facilities, namely Bogor, Bandung, Karawang, Bekasi, and Bandung City. The second cluster consists of 7 districts/municipalities with medium availability, namely Sukabumi, Cianjur, Garut, Tasikmalaya, Ciamis, Cirebon, and Indramayu. The third cluster consists of 15 districts/municipalities with low availability of health facilities. This clustering provides a comprehensive picture of the patterns and disparities in the availability of health facilities in West Java that can be utilized to improve the quality of health services.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectK-Meansen_US
dc.subjectHealth Facilitiesen_US
dc.subjectSDGsen_US
dc.titlePengaplikasian Analisis Cluster K-Means dalam Pengelompokan Kabupaten/Kota di Jawa Barat Berdasarkan Sarana Kesehatanen_US
dc.title.alternativeApplication of K-Means Cluster Analysis in Grouping Districts/Cities in West Java Based on Health Facilitiesen_US
dc.typeThesisen_US
dc.identifier.nimNIM212407016
dc.identifier.nidnNIDN0026106305
dc.identifier.kodeprodiKODEPRODI49401#Statistika
dc.description.pages56 Pagesen_US
dc.description.typeKertas Karya Diplomaen_US


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