Covid-19 Risk Mapping with the Fuzzy C-Means Method in DIY Province
Keywords:
Keywords: covid-19, clustering, fuzzy c-means.Abstract
Abstract.The Fuzzy C-Means method is one of the grouping methods that can be used by the cluster model. This method can be used to classify various case data, one of which is related to Covid-19. This research is intended to determine the level of risk of Covid-19 based on several determining variables in the province of Yogyakarta Special Region (DIY) during the last 2 months. The data is processed using fuzzy c-means clustering (FCM) analysis which is a development of fuzzy clustering analysis with c participation to analyze the Covid-19 pandemic in DIY province based on several determining variables, namely the number of additional positive victims, pdp , and the frequency of recovered patients. The number of additional positive victims, pdp, odp and frequency of recovered patients were used as variables in grouping regions by district based on the level of risk to Covid-19. The best grouping results are obtained based on "High Risk" and "Low Risk". Districts that are included in the High Risk group are Sleman and Yogyakarta Regencies. Meanwhile, other districts are included in the Low Risk group, namely Bantul, Kulon Progo, and Gunungkidul Regencies.
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Published
2021-02-28
How to Cite
Tarigan, E. C. B., Artika, R., Febriawan, M. R., Muna, K. N., & Putri, . S. H. H. (2021). Covid-19 Risk Mapping with the Fuzzy C-Means Method in DIY Province. Proceeding International Conference on Science and Engineering, 4, 259–265. Retrieved from http://sunankalijaga.org/prosiding/index.php/icse/article/view/668
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