Cluster Analysis of Covid-19 in Indonesia Using K-means Method

Claudia Larasvaty, S. Khomsah, R. Sa
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Abstract

These days technology are rapidly increasing and developing in various fields, especially data storage. The information that has been stored in a database usually called a dataset. Covid-19 is a new type of respiratory disease that attacks the respiratory system with rapid transmission, followed by the increasing number of Covid-19 cases that continues to increase every day in all provinces in Indonesia. This study aims to cluster the spread of Covid-19 in every province in Indonesia by using the data that obtained from the website named kaggle with many data variables. The method used in this research is K-Means. From many variables in the data, for this study only 3 variables were taken, which are: Number of Recovery, Number of Deaths, and Number of total Cases in Covid-19 in Indonesia. These 3 variables then will be applied using the K-Means method and formed 3 provincial groups. By using the clustering method and the K-means algorithm, this research can be carried out to find the characteristics of the distribution in each province in Indonesia by looking at the best clusters.
基于K-means方法的印度尼西亚Covid-19聚类分析
近年来,技术在各个领域迅速发展,尤其是数据存储。存储在数据库中的信息通常称为数据集。Covid-19是一种攻击呼吸系统的新型呼吸系统疾病,传播迅速,随后印度尼西亚所有省份的Covid-19病例数量持续增加,每天都在增加。本研究旨在通过使用从名为kaggle的网站获得的具有许多数据变量的数据,将Covid-19在印度尼西亚每个省的传播聚集在一起。本研究使用的方法是K-Means。从数据中的许多变量中,本研究仅选取了3个变量,分别是:印度尼西亚Covid-19的康复人数、死亡人数和总病例数。然后将使用K-Means方法应用这3个变量并形成3个省组。本研究采用聚类方法和K-means算法,通过寻找最佳聚类,找出印尼各省份的分布特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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