Analisa Kasus Covid-19 Menggunakan Metode Agglomerative Hierarchical Clustering pada Tingkat Kematian

Coronavirus disease was first discovered in the city of Wuhan, China in December 2019. Due to the impact of the new coronavirus infection, many infected patients have died including in the United States. Data obtained from the official web of Data Centers for Disease Control and Prevention shows a high mortality rate due to Covid-19. This study aims to analyze the Covid-19 mortality rate by region using the Agglomerative Hierarchical Clustering (AHC) method and find the optimal cluster validity using the Silhouette Index (SI) method. Clustering the Covid-19 death rate using the AHC method is needed to understand the pattern of death rates due to Covid-19 and assist in making policies for pandemic prevention and handling. This research resulted in the optimal number of clusters at n clusters = 2 with cluster-1 high mortality rate of 12,307 object and cluster-2 low mortality rate of 13,498 object. The results of this study can thoroughly analyze Covid-19 death data such as revealing some important findings and input that can be proposed to improve the quality of response to future pandemics.