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All Journal Jurnal SOLMA QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT JABE (Journal of Applied Business and Economic) SAP (Susunan Artikel Pendidikan) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Pendidikan dan Kebudayaan (JURDIKBUD) Kapas: Kumpulan Artikel Pengabdian Masyarakat
Dian Novita
Universitas Indraprasta PGRI
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : STRING (Satuan Tulisan Riset dan Inovasi Teknologi)

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Analisis Clustering Virus MERS-CoV Menggunakan Metode Spectral Clustering Dan Algoritma K-Means Septian Wulandari; Dian Novita
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 3 (2021)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.5 KB) | DOI: 10.30998/string.v5i3.7942

Abstract

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.
Co-Authors Azhar Rimawan Ilyas Bay Haqi Eko Yunianto Wibowo Fitriana Destiawati Fitriana Destiawati Harun Suswanto Ibnu Bagoes Irawan Lin Suciani Astuti Luthfiani Miftah Rahmah Ni Wayan Parwati Nurfidah Dwitiyanti Rezkiyanah Hikmah Septian Wulandari Sutrisno, Sutrisno Tondi Jubaedi Wilson, Agus