KHAIRUNNISA, KHAIRUNNISA and Hanum, Herlina and Ngudiantoro, Ngudiantoro (2023) PENGAPLIKASIAN METODE K-MEANS DALAM PENGELOMPOKAN PROVINSI BERDASARKAN JUMLAH SUMBER DAYA MANUSIA (SDM) KESEHATAN PUSKESMAS DI INDONESIA TAHUN 2021. Undergraduate thesis, Sriwijaya University.
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Abstract
The Puskesmas is a technical implementation unit for the district/city health office that is responsible for implementing health development in a work area. One of the factors for the realization of healthy Indonesian citizens is that they must be supported by public health service facilities. Health human resources are health workers (including strategic health workers) and supporting staff who are involved and work and devote themselves to health efforts and management. The purpose of this study is to group provinces in Indonesia and find out the characteristics of each group produced by the K-Means method based on data on the number of Public Health Center Human Resources in Indonesia in 2021. Based on the k-means cluster analysis from the results of the Silhouette Coeficent calculation by testing the number of 3 clusters up to 6 clusters an a total of 3 clusters is obtained as a cluster with a value optimal and produces the largest Silhouett value, namely 0.78813. It can be concluded that 3 clusters were the best cluster, where in cluster 1 there is 1 province which includes a province that has low category health workers, namely Bali Province, cluster 2 consists of 3 provinces which have high category health workers, namely West Java, Central Java and Java. East and cluster 3 consists of 30 other provinces that have medium category health workers.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Sumber Daya Manusia Kesehatan, Puskesmas, K-Means Klaster |
| Subjects: | Q Science > QA Mathematics > QA1-43 General |
| Divisions: | 08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1) |
| Depositing User: | Khairunnisa Khairunnisa |
| Date Deposited: | 11 Apr 2023 01:38 |
| Last Modified: | 11 Apr 2023 01:38 |
| URI: | http://repository.unsri.ac.id/id/eprint/94237 |
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