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The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data

Author

Listed:
  • Dahlan Abdullah

    (Universitas Malikussaleh)

  • S. Susilo

    (Universitas Muhammadiyah Prof. Dr. Hamka)

  • Ansari Saleh Ahmar

    (Universitas Negeri Makassar)

  • R. Rusli

    (Universitas Negeri Makassar)

  • Rahmat Hidayat

    (Department of Information Technology)

Abstract

This study was conducted with the aim to the clustering of provinces in Indonesia of the risk of the COVID-19 pandemic based on coronavirus disease 2019 (COVID-19) data. This clustering was based on the data obtained from the Indonesian COVID-19 Task Force (SATGAS COVID-19) on 19 April 2020. Provinces in Indonesia were grouped based on the data of confirmed, death, and recovered cases of COVID-19. This was performed using the K-Means Clustering method. Clustering generated 3 provincial groups. The results of the provincial clustering are expected to provide input to the government in making policies related to restrictions on community activities or other policies in overcoming the spread of COVID-19. Provincial Clustering based on the COVID-19 cases in Indonesia is an attempt to determine the closeness or similarity of a province based on confirmed, recovered, and death cases. Based on the results of this study, there are 3 clusters of provinces.

Suggested Citation

  • Dahlan Abdullah & S. Susilo & Ansari Saleh Ahmar & R. Rusli & Rahmat Hidayat, 2022. "The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1283-1291, June.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:3:d:10.1007_s11135-021-01176-w
    DOI: 10.1007/s11135-021-01176-w
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    Cited by:

    1. Ye Yuan & Jiaqi Wang & Xin Xu & Ruoshi Li & Yongtong Zhu & Lihong Wan & Qingdu Li & Na Liu, 2023. "Alleviating Long-Tailed Image Classification via Dynamical Classwise Splitting," Mathematics, MDPI, vol. 11(13), pages 1-12, July.
    2. Adi Jafar & Ramli Dollah & Prabhat Mittal & Asmady Idris & Jong Eop Kim & Mohd Syariefudin Abdullah & Eko Prayitno Joko & Dayangku Norasyikin Awang Tejuddin & Nordin Sakke & Noor Syakirah Zakaria & Mo, 2023. "Readiness and Challenges of E-Learning during the COVID-19 Pandemic Era: A Space Analysis in Peninsular Malaysia," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    3. Adi Jafar & Ramli Dollah & Ramzah Dambul & Prabhat Mittal & Syahruddin Awang Ahmad & Nordin Sakke & Mohammad Tahir Mapa & Eko Prayitno Joko & Oliver Valentine Eboy & Lindah Roziani Jamru & Andika Ab. , 2022. "Virtual Learning during COVID-19: Exploring Challenges and Identifying Highly Vulnerable Groups Based on Location," IJERPH, MDPI, vol. 19(17), pages 1-16, September.
    4. Saemi Shin & Won Suck Yoon & Sang-Hoon Byeon, 2022. "Trends in Occupational Infectious Diseases in South Korea and Classification of Industries According to the Risk of Biological Hazards Using K-Means Clustering," IJERPH, MDPI, vol. 19(19), pages 1-19, September.

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