Open AccessImplementasi Algoritma Clustering Partitioning Around Medoid (PAM) dalam Clustering Virus MERS-Cov
Author(s) -
Septian Wulandari,
Nurfidah Dwitiyanti
Publication year - 2020
Publication title -
string (satuan tulisan riset dan inovasi teknologi)
Language(s) - English
Resource type - Journals
eISSN - 2549-2837
pISSN - 2527-9661
DOI - 10.30998/string.v5i1.6469
Subject(s) - cluster analysis , medoid , virus , silhouette , computer science , middle east respiratory syndrome coronavirus , biology , genetics , artificial intelligence , covid-19 , medicine , disease , pathology , infectious disease (medical specialty)
Th e Middle East Respiratory Coronavirus (MERS-CoV) is a disease caused by a coronavirus. This virus is contagious, but its transmission is not as easy as the common cold, MERS-CoV virus is better susceptible to transmitting through direct contact, for example in people who care about the MERS-CoV virus without the need for virus protection. To determine the characteristics, the MERS-CoV disease virus can be identified by identifying DNA (deoxyribonucleic acid). One technique in understanding the characteristics of life is by grouping. Grouping can be done by grouping DNA into groups that have attributes and functions. The Clustering algorithm used in this study is Partitioning Around Medoid (PAM). This algorithm has the advantage that the results of the grouping process are not by following the order of entering the dataset and overcome sensitivity to noise and outliers. The purpose of this study is to implement the Partitioning Around Medoid (PAM) clustering algorithm in clustering the MERS-CoV virus. This research was conducted through a quantitative descriptive literature study. The implementation of the PAM algorithm on the MERS-CoV DNA sequence obtained 2 clusters with the highest silhouette coefficient value on the number of clusters 2, namely 0.61534. The number of members in Cluster 1 is 84 MERS-CoV DNA sequences and the number of members in Cluster 2 is 16 MERS-CoV DNA sequences.