Clustering Area Covid-19 Indonesia With K-Means (Case study : Kaggle Dataset)

Authors

  • Heti Mulyani Politeknik Enjinering Indorama
  • Ricak Agus Setiawan Politeknik Enjinering Indorama, Indonesia
  • Musawarman Politeknik Enjinering Indorama, Indonesia
  • Annisa Romadloni Politeknik Negeri Cilacap

DOI:

https://doi.org/10.56873/jitu.5.2.4894

Keywords:

covid19, province, k-means, clustering

Abstract

The spread of the coronavirus in Indonesia is quite fast. The spread of Covid 19 is almost evenly distributed in all provinces in Indonesia. Some areas even have a fairly high mortality rate. Therefore, it is necessary to group regions to find out which areas have the highest to lowest Covid cases so that the appropriate response process can be carried out. In addition, data visualization is also needed that provides information on COVID-19 data for each province. In this study, the data were grouped using the K-Means Clustering method. The dataset used is the Indonesian Covid-19 dataset from Kaggle. The criteria for each province's covid cluster are the number of cases and deaths. The Clustering process uses the Python programming language. From the results of this study, it can be seen that there are 3 groups of covid. The first group consists of 30 provinces with several cases below 200,000 and a number of deaths below 6000. The second group contains two provinces that have the highest number of cases, namely above 600,000, but the number of deaths is less than group 3, which is 15000. In group 3 there are 2 provinces where the number of cases is below 500,000 but the death rate is above 30,000.

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Published

2022-12-28

Issue

Vol. 5 No. 2 (2022): December 2022

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Artikel

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How to Cite

Clustering Area Covid-19 Indonesia With K-Means (Case study : Kaggle Dataset). (2022). Journal of Information Technology and Its Utilization, 5(2), 41-46. https://doi.org/10.56873/jitu.5.2.4894