Identification of Tuberculosis Patient Characteristics Using K-Means Clustering

Identification of Tuberculosis Patient Characteristics Using K-Means Clustering

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In Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to...

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Main Author: Betha Nur Sari
Format: Article
Language:English
Published: Jurusan Ilmu Komputer Universitas Negeri Semarang 2016-11-01
Series:Scientific Journal of Informatics
Subjects:
characteristic, clustering, K-means, patient, tuberculosis
Online Access:https://journal.unnes.ac.id/nju/index.php/sji/article/view/7909
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spelling doaj-834f98865bd9493293f87d1f480c26e72020-11-24T22:28:07ZengJurusan Ilmu Komputer Universitas Negeri SemarangScientific Journal of Informatics2407-76582460-00402016-11-013212913810.15294/sji.v3i2.79095178Identification of Tuberculosis Patient Characteristics Using K-Means ClusteringBetha Nur Sari0University of Singaperbangsa KarawangIn Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to identify the characteristics of tuberculosis patients. The results of K-means clustering validated by gene shaving and silhoutte coefficient. The experiment results indicate the optimum clusters value obtained from the K-mean clustering that has been validated by gene shaving and silhouette coefficient. K-means clustering divided four groups of tuberculosis patients based on their characteristics. There were divided at a category of disease (pulmonary TB, Extra Pulmonary TB and both), the age of the patient and the results of treatment of tuberculosis.https://journal.unnes.ac.id/nju/index.php/sji/article/view/7909characteristic, clustering, K-means, patient, tuberculosis
collection DOAJ
language English
format Article
sources DOAJ
author Betha Nur Sari
spellingShingle Betha Nur Sari
Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
Scientific Journal of Informatics
characteristic, clustering, K-means, patient, tuberculosis
author_facet Betha Nur Sari
author_sort Betha Nur Sari
title Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
title_short Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
title_full Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
title_fullStr Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
title_full_unstemmed Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
title_sort identification of tuberculosis patient characteristics using k-means clustering
publisher Jurusan Ilmu Komputer Universitas Negeri Semarang
series Scientific Journal of Informatics
issn 2407-7658
2460-0040
publishDate 2016-11-01
description In Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to identify the characteristics of tuberculosis patients. The results of K-means clustering validated by gene shaving and silhoutte coefficient. The experiment results indicate the optimum clusters value obtained from the K-mean clustering that has been validated by gene shaving and silhouette coefficient. K-means clustering divided four groups of tuberculosis patients based on their characteristics. There were divided at a category of disease (pulmonary TB, Extra Pulmonary TB and both), the age of the patient and the results of treatment of tuberculosis.
topic characteristic, clustering, K-means, patient, tuberculosis
url https://journal.unnes.ac.id/nju/index.php/sji/article/view/7909
work_keys_str_mv AT bethanursari identificationoftuberculosispatientcharacteristicsusingkmeansclustering
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