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

SARI, NOVITA and Tania, Ken Ditha (2022) CLUSTERING KABUPATEN/ KOTA BERDASARKAN CAPAIAN KUALITAS PELAYANAN KESEHATAN IBU MENGGUNAKAN METODE K-MEANS (STUDI KASUS : DINAS KESEHATAN PROVINSI SUMSEL). Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Maternal Mortality Rate, Data Mining, CRISPDM, Clustering, K-Means
Subjects:	Q Science > Q Science (General) > Q1-295 General
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	NOVITA SARI
Date Deposited:	28 Jul 2022 03:39
Last Modified:	28 Jul 2022 03:39
URI:	http://repository.unsri.ac.id/id/eprint/75053

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