Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia

Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under...

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Autor principal:	Rini Silvi
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2018
Materias:	Clustering Centroid Linkage K-means Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/7805cd4899904836bc52c014e95a035f
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spelling	oai:doaj.org-article:7805cd4899904836bc52c014e95a035f2021-12-02T17:37:07ZAnalisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia2527-31592527-316710.15642/mantik.2018.4.1.22-31https://doaj.org/article/7805cd4899904836bc52c014e95a035f2018-05-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/263https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing Sw/Sb ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.Rini SilviDepartment of Mathematics, UIN Sunan Ampel SurabayaarticleClusteringCentroid LinkageK-meansMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 4, Iss 1, Pp 22-31 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clustering Centroid Linkage K-means Mathematics QA1-939
spellingShingle	Clustering Centroid Linkage K-means Mathematics QA1-939 Rini Silvi Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
description	Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing Sw/Sb ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.
format	article
author	Rini Silvi
author_facet	Rini Silvi
author_sort	Rini Silvi
title	Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
title_short	Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
title_full	Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
title_fullStr	Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
title_full_unstemmed	Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia
title_sort	analisis cluster dengan data outlier menggunakan centroid linkage dan k-means clustering untuk pengelompokkan indikator hiv/aids di indonesia
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2018
url	https://doaj.org/article/7805cd4899904836bc52c014e95a035f
work_keys_str_mv	AT rinisilvi analisisclusterdengandataoutliermenggunakancentroidlinkagedankmeansclusteringuntukpengelompokkanindikatorhivaidsdiindonesia
_version_	1718379890516951040

Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia

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