Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
Abstract: 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.
Author: Betha Nur Sari
Journal Code: jptinformatikagg160028
