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CLUSTERING KABUPATEN/ KOTA BERDASARKAN CAPAIAN KUALITAS PELAYANAN KESEHATAN IBU MENGGUNAKAN METODE K-MEANS (STUDI KASUS : DINAS KESEHATAN PROVINSI SUMSEL)

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Penilaian

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One of the benchmarks for the success of development is the degree of public health, which can be seen from the Maternal Mortality Rate (MMR). The high MMR and IMR indicate the low quality of health services for mothers and children. This study aims to apply data mining in the process of finding district/city cluster patterns that can assist the South Sumatra Provincial Health Office in controlling the quality of maternal health services. The method used is CRISP-DM. The data used is data on factors that affect MMR in 2020 as many as 170 records consisting of 17 districts/cities and 10 attributes. Clustering algorithm used is K-Means with Euclidean Distance formula. The grouping is divided into 4 clusters, namely: Poor (cluster_0), Enough (cluster_1), Good (cluster_2), Very Good (cluster_3). The iteration process took place 4 times with the results of 4 regencies/cities (poor conditions), 9 regencies/cities (enough conditions), 1 regencies/cities (good conditions) and 1 regencies/cities (very good conditions). Clustering evaluation with DBI resulted in the best k value, namely 4 clusters of 0.479.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002484	T75053	T750532022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T750532022

Publisher

Inderalaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Sriwijaya., 2022

Collation

xiv, 62 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Pengolahan Data
Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

SEPTA

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