Ferdinand, Ferry Vincenttius and Sebastian, Johan and Nata, Christopher and Natalia, Friska and Adiwena, Stevanus (2022) Covid-19 Clustering by Province: A Case Study of Covid-19 Cases in Indonesia. ICIC Express Letters, 13 (4). pp. 389-396. ISSN 2185-2766
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Abstract
The 2019 novel coronavirus disease (COVID-19) pandemic in Indonesia has caused issues in many sectors such as health, economy, and education. Several actions had been taken by the government to prevent and forestall the spread of the coronavirus infection. However, right now there are still many new cases emerging especially in cities with dense population. In the meantime, actions taken from the government are based on the classification of the severity of new cases; there are red zone, yellow zone and green zone. Therefore, mapping cities into zone is critical because it concerns the right decision to be implemented. This paper aimed to cluster the severity of each province in Indonesia based on the number of cases, recovered, and casualties using 3 clustering methods namely K-Means, K-Medoids, and Gaussian mixture model. The result shows that the most optimal clustering method is the Gaussian mixture model, while the least optimal method for clustering is the K-Means. Furthermore, it is also discovered that the cluster always changes overtime, and the cluster can shift depending on the corresponding parameter.
| Item Type: | Article |
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| Keywords: | Indonesia, COVID-19, Clustering analysis, K-Means, K-Medoids, Gaussian mixture model |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 001 Knowledge > 001.4 Research 300 Social Sciences > 310 Statistics |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 06 Nov 2023 07:12 |
| Last Modified: | 06 Nov 2023 07:12 |
| URI: | https://kc.umn.ac.id/id/eprint/27040 |
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