Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
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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| Format: | Article |
| Language: | English |
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Universitas Negeri Semarang
2016-11-01
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| Series: | Scientific Journal of Informatics |
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| Online Access: | http://journal.unnes.ac.id/sju/index.php/sji/article/view/7909 |
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| author | Betha Nur Sari |
| author_facet | Betha Nur Sari |
| author_sort | Betha Nur Sari |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-244af4e68799462aadd44835c2a142c8 |
| institution | OA Journals |
| issn | 2407-7658 2460-0040 |
| language | English |
| publishDate | 2016-11-01 |
| publisher | Universitas Negeri Semarang |
| record_format | Article |
| series | Scientific Journal of Informatics |
| spelling | doaj-art-244af4e68799462aadd44835c2a142c82025-08-20T02:04:43ZengUniversitas Negeri SemarangScientific Journal of Informatics2407-76582460-00402016-11-01323140Identification 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.http://journal.unnes.ac.id/sju/index.php/sji/article/view/7909characteristic, clustering, K-means, patient, tuberculosis |
| spellingShingle | Betha Nur Sari Identification of Tuberculosis Patient Characteristics Using K-Means Clustering Scientific Journal of Informatics characteristic, clustering, K-means, patient, tuberculosis |
| title | 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_short | Identification of Tuberculosis Patient Characteristics Using K-Means Clustering |
| title_sort | identification of tuberculosis patient characteristics using k means clustering |
| topic | characteristic, clustering, K-means, patient, tuberculosis |
| url | http://journal.unnes.ac.id/sju/index.php/sji/article/view/7909 |
| work_keys_str_mv | AT bethanursari identificationoftuberculosispatientcharacteristicsusingkmeansclustering |