System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method

Compiler

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Field Value
 
Title System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method
 
Creator Suhirman, Suhirman
Wintolo, Hero
 
Subject
Health Level, Health Indicators, Agglomerative Hierarchical Clustering, Cluster
 
Description Regions having higher level of welfare do not always have better indicator values than other regions having lower level of welfare. The problem is the lack of information related to the indicator values needed to determine the health level. Therefore, clustering using health data becomes necessary. Data were clustered to see the maximum or the minimum level of similarity. The clustered data were based on the similarity of four morality indicator values of the regional health level. Morality indicator values used in this research are infant mortality rate, child mortality rate, maternal mortality rate, and rough birth rate. The method used is Agglomerative Hierarchical Clustering (AHC) - Complete Linkage. Data were calculated using Euclidean Distance Equation, then Complete Linkage. Four clustered data were grouped into two clusters, healthy and/or unhealthy. The result, combining from all clusters into two large clusters to see healthy and unhealthy results.
 
Publisher Sekolah Tinggi Teknologi Adisutjipto Yogyakarta
 
Contributor
 
Date 2019-03-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ejournals.stta.ac.id/index.php/compiler/article/view/425
10.28989/compiler.v8i1.425
 
Source Compiler; Vol 8, No 1 (2019): Mei; 95-104
2549-2403
2252-3839
10.28989/compiler.v8i1
 
Language eng
 
Relation http://ejournals.stta.ac.id/index.php/compiler/article/view/425/pdf
 
Rights Copyright (c) 2019 Compiler
 

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