Fitria Virgantari, Yasmin Erika Faridhan

Judul	:	K-Means Clustering of COVID-19 Cases in Indonesia’s Provinces
Abstrak	:	The novel coronavirus disease (COVID-19) has been rapidly spreading, causing a severe health crisis all around the world, including Indonesia.  As expected, due to Indonesia’s diverse topography and population, there are variations in the number of cases amongst its provinces.  Therefore clustering is needed to develop a map of COVID-19 cases to enable optimal handling of this pandemic. The provinces are clustered using K-means method according to their respective COVID-19 case numbers. Data taken from Indonesian Ministry of Health in November 2020 is used in this study, covering COVID-19 cases in Indonesia’s 34 provinces. K-means results in seven optimal clusters with variance ratio of 0.185. Clusters 1 to 3 cover most provinces in Java, including DKI Jakarta in Cluster 1 as the province with the most cases. Each of Clusters 4 and 5 consists of 5 provinces, while each of Clusters 6 and 7 consists of 10 provinces.  Cluster 7 comprises provinces with lowest cases of COVID-19.
Tahun	:	2020	Media Publikasi	:	Seminar Internasional
Kategori	:	Makalah/Koran	No/Vol/Tahun	:	1 / 1 / 2020
ISSN/ISBN	:	1
PTN/S	:	Universitas Pakuan	Program Studi	:	MATEMATIKA
Bibliography	:	Barakbah, A. R., and Arai, K.,  Determining constraints of moving variance to find global optimum and make automatic clustering, Proceedings of Industrial Electronics Seminar (IES), Surabaya, Indonesia, October 12, pp. 409-413, 2004. Berkhin, P., A survey of clustering data mining techniques, Grouping Multidimensional Data, Springer, Berlin, pp. 25-71, 2006. Dwitri, N., Tampubolon, J. A., Prayoga, S., Zer, F. I. R. H., and Hartama, D., Penerapan algoritma K-Means dalam menentukan tingkat penyebaran pandemi COVID-19 di Indonesia, Jurnal Teknologi Informasi, vol. 4, no. 1, pp. 128-132, 2020. Johnson, R. A., and Wichern, D. W., Applied Multivariate Statistical Analysis, 6th edition, Prentice-Hall International, New Jersey, 2007. Kementerian Kesehatan Republik Indonesia, Infeksi Emerging Downloads, Available: https://infeksiemerging.kemkes.go.id/downloads/?dl_cat=5&dl_page=2#.X8SjEs0zbIU, November 8, 2020. Ma, E. W. M., and Chow, T. W. S., A new shifting grid clustering algorithm, Pattern Recognition, vol. 37, pp. 503-514, 2004. Mahmoudi, M. R., Baleanu, D., Mansor, Z., Tuan, B. A., and Pho, K.-H., Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries, Chaos, Solitons and Fractals, vol. 140, 2020. Mattjik, A. A., and Sumertajaya, I. M., Sidik Peubah Ganda, IPB Press, Bogor, 2011. Nugroho, S., Statistik Multivariat Terapan, UNIB Press, Bengkulu, 2008. Our World in Data, Mortality Risk of COVID-19, Available: https://ourworldindata.org/mortality-risk-covid#the-case-fatality-rate, November 27, 2020. Ray, S., and Turi, R. H., Determination of number of clusters in K-means clustering and application in colour image segmentation, Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, Calcutta, India, December 27-29, pp. 137-143, 1999. Solichin, A., and Khairunnisa, K., Klasterisasi persebaran virus Corona (Covid-19) di DKI Jakarta menggunakan metode K-Means, Fountain of Informatics Journal, vol. 5, no. 2, pp. 52-59, 2020. World Health Organization (WHO), Estimating mortality from COVID-19, Available: https://www.who.int/news-room/commentaries/detail/estimating-mortality-from-covid-19, August 4, 2020. Zarikas, V., Poulopoulos, S. G., Gareiou, Z., and Zervas, E., Clustering analysis of countries using the COVID-19 cases dataset, Data in Brief, vol. 31, 2020.
URL	:	http://icogoia.worldconference.id/