Garuda - Garba Rujukan Digital

Suhirman

Universitas Teknologi Yogyakarta

Author-ID : 5739092

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method Suhirman, Suhirman; Wintolo, Hero
Compiler Vol 8, No 1 (2019): Mei
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

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.

System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method Suhirman Suhirman; Hero Wintolo
Compiler Vol 8, No 1 (2019): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

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.

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