Clustering the Spread of Tuberculosis Disease in Aceh Tamiang District Using K-Medoids Method

Authors

  • Rania Sofia Salsabila Harahap Rania Universitas Malikussaleh
  • Eva Darnila University Malikussaleh
  • Yesy Afrillia University Malikussaleh

Keywords:

clustering, K-Medoid, tuberculosis

Abstract

This research aims to develop a web-based system using the K-Medoids method to classify Tuberculosis (TB) disease spread in Aceh Tamiang Regency.  TB is a severe health problem in Indonesia that requires effective handling, especially in identifying areas with high levels of spread. The data used in this study includes the number of TB patients from 2019 to 2023 in 12 sub-districts, with five types of TB, namely Pulmonary TB, Extra Pulmonary TB, Latent TB, Billiary TB, and Drug Resistant TB. This study used the K-Medoids algorithm with a value of K=3. This clustering shows that the clustering for Pulmonary TB disease type obtained C1 by 16.67% or two sub-districts, C2 by 8.33% or one sub-district, and C3 by 75% or nine sub-districts. In the Extra Pulmonary TB disease type, C1 is 25%, C2 is 8.33% and C3 is 66.67%. C1 8.33%, C2 58.33% and C3 33.33% were obtained for the type of Latent TB disease. For the type of Billiary TB disease, there are C1 8.33%, C2 58.33% and C3 33.33%. For the drug-resistant TB, C1 16.67%, C2 16.67%, and C3 66.67% are used. The average deviation for the 5 types of disease clusters is 2.01. The results of this study are expected to be a reference for local governments in efforts to prevent and manage the spread of TB disease in Aceh Tamiang District.

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Published

2024-12-27

Issue

Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Section

Articles

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Copyright (c) 2024 Rania Sofia Salsabila Harahap Rania, Eva Darnila, Yesy Afrillia

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