Analisis Clustering Virus MERS-CoV Menggunakan Metode Spectral Clustering Dan Algoritma K-Means

Abstract

<p><em>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.</em></p>

Penerapan Machine Learning Clustering K-Means dan Linear Regression Dalam Penentuan Tingkat Resiko Tuberkulosis Paru

“…hasil dari data yang dinormalisasi tersebut kemudian di cluster menggunakan algoritma partisi k-means. Hasil penelitian menunjukkan bahwa hasil clustering dengan algoritma k-means tiga cluster dan lebih homogen dibandingkan clustering yang hanya menggunakan k-means [9].…”