The 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.