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Nor Hamidah
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBARAN COVID-19 MENGGUNAKAN MODEL-BASED CLUSTERING t-MULTIVARIAT Nor Hamidah; Rukun Santoso; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i1.33999

Abstract

The spread of Covid-19 had a significant impact in all sectors. Enforcement policies from the government that are appropriate with the conditions for the spread of the virus that are needed to prevent a bigger impact. Clusteritation by province based on data on the spread of Covid-19 is important for the government to set appropriate policies in order to prevent the spread of Covid-19. The data used include data on population density, testing rate, proportion of population 50 years and over, and proportion of population diligently hand-washing in each province. The data factors for the spread of Covid-19 tend to overlap and there are outliers in the data which causes the data not normally distributed. In this study, Model-Based Clustering t-multivariate was used for data clustering. The results show that using Integrated Completed Likelihood, two groups of optimal cluster were obtained. The second cluster has a higher risk of spreading Covid-19 than the first cluster. Keywords : Covid-19, Clustering, Model-Based Clustering t-Multivariat
Co-Authors Agus Rusgiyono Rukun Santoso