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All Journal Contemporary Mathematics and Applications (ConMathA)
Ira Yudistira
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Materials Science & Nanotechnology Mathematics

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Determining Optimal Hierarchical Clustering by Combining Needleman Wunsch and Jukes Cantor Algorithms in Tuberculosis (TB) Disease Clustering Hildatul Anizah; Tony Yulianto; Kuzairi; Ira Yudistira; Amalia, Rica
Contemporary Mathematics and Applications (ConMathA) Vol. 7 No. 1 (2025)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v7i1.64172

Abstract

Tuberculosis (TBC) is an infectious disease affecting the respiratory system, caused by the bacterium Mycobacterium tuberculosis. Tuberculosis (TBC) remains a global concern, and to date, no country is completely free from TB. This disease continues to be one of the leading causes of mortality. Therefore, it is essential to categorize the spread of TBC. The percentage of identity in genetic codes will reveal the proportion of mutations. The percentage of identity in genetic codes will demonstrate that, although the symptoms caused by a disease may be quite similar, the protein sequences are not necessarily the same. In this study, the researchers employed the Hierarchical Clustering method, integrating the Needleman-Wunsch and Jukes-Cantor algorithms, resulting in two groups. The first group consists of 9 interconnected rows, while the second group consists of 7 interconnected rows.
Co-Authors Amalia, Rica Kuzairi Tony Yulianto