Open AccessAnalisis Clustering Virus MERS-CoV Menggunakan Metode Spectral Clustering Dan Algoritma K-Means
Author(s) -
Septian Wulandari,
Dian Novita
Publication year - 2021
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.v5i3.7942
Subject(s) - cluster analysis , virus , partition (number theory) , spectral clustering , computer science , data mining , artificial intelligence , mathematics , biology , virology , combinatorics
The MERS-Cov virus has spread to other countries outside Saudi Arabia. This is because the MERS-CoV virus can mutate rapidly so it is feared that it could threaten public health and even world health. This virus develops and becomes an acute respiratory disease and the mortality rate reaches 30% among 536 cases. One way to classify the MERS-CoV virus is by grouping the DNA sequences of the MERS-CoV virus which have similar characteristics and functions. Spectral clustering is a grouping method that can identify DNA gene expression. This method is also able to partition DNA data with a more complex structure than the partition clustering method. The purpose of this study was to analyze the MERS-CoV virus clustering using the spectral clustering method and the k-means algorithm. This study used a quantitative descriptive literature approach. The results showed that the results of clustering using the spectral clustering method and the k-means algorithm produced three clusters and were more homogeneous than clustering using k-means only.