AN ANALYSIS OF CLUSTER TIMES SERIES FOR THE NUMBER OF COVID-19 CASES IN WEST JAVA

Authors

  • Nurfitri Imro'ah Universitas Tanjungpura
  • Nur'ainul Miftahul Huda Universitas Tanjungpura

DOI:

https://doi.org/10.25077/jmua.12.3.203-212.2023

Keywords:

ACF distance cluster, hierarchy, Silhouette coefficient

Abstract

The government may be able to develop more effective strategies for dealing with COVID-19 cases if it groups districts and cities according to the features of the number of Covid-19 cases being reported in each district or city. The data can be more easily summarized with the help of cluster analysis, which organizes items into groups according to the degree of similarity between members. Since it is possible to group more than one period together, the generation of clusters based on time series is a more efficient method than clusters that are created for each individual unit. Using a time series cluster hierarchical technique that has complete linkage, the purpose of this study is to categorize the number of instances of Covid-19 that have been found in West Java by district or city. The data that was used comes from monthly reports of Covid-19 instances compiled by West Java districts from 2020 to 2022. The Autocorrelation Function (ACF) distance cluster was utilized in this investigation to determine how closely cluster members are related to one another. According to the findings, there could be as many as seven separate clusters, each including a unique assortment of districts and cities. Cluster 3, which is comprised of three different cities and regencies, including Bandung City, West Bandung Regency, and Sumedang Regency, has an average number of cases that is 66, making it the cluster with the highest number of cases overall. A value of 0.2787590 is obtained for the silhouette coefficient as a result of the established grouping. This value suggests that the structure of the newly created cluster is quite fragile.The government may be able to develop more eective strategies fordealing with COVID-19 cases if it groups districts and cities according to the featuresof the number of Covid-19 cases being reported in each district or city. The data canbe more easily summarized with the help of cluster analysis, which organizes items intogroups according to the degree of similarity between members. Since it is possible togroup more than one period together, the generation of clusters based on time series isa more ecient method than clusters that are created for each individual unit. Using atime series cluster hierarchical technique that has complete linkage, the purpose of thisstudy is to categorize the number of instances of Covid-19 that have been found in WestJava by district or city. The data that was used comes from monthly reports of Covid-19 instances compiled by West Java districts from 2020 to 2022. The AutocorrelationFunction (ACF) distance cluster was utilized in this investigation to determine howclosely cluster members are related to one another. According to the ndings, there couldbe as many as seven separate clusters, each including a unique assortment of districtsand cities. Cluster 3, which is comprised of three dierent cities and regencies, includingBandung City, West Bandung Regency, and Sumedang Regency, has an average numberof cases that is 66, making it the cluster with the highest number of cases overall. Avalue of 0.2787590 is obtained for the silhouette coecient as a result of the establishedgrouping. This value suggests that the structure of the newly created cluster is quitefragile.

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References

Knote, R., Janson, A., Söllner, M., and Leimeister, J. M., 2019, Classifying Smart Personal Assistants: An Empirical Cluster Analysis. https://doi.org/10.24251/HICSS.2019.245

Ali, A., and Sheng-Chang, C., 2020, Characterization of well logs using K-mean cluster analysis, Journal of Petroleum Exploration and Production Technology, Volume : 10(6), 2245–2256. https://doi.org/10.1007/s13202-020-00895-4

Abualigah, L. M., Khader, A. T., and Hanandeh, E. S., 2018, Hybrid clustering analysis using improved krill herd algorithm, Applied Intelligence, Volume : 48(11), 4047–4071. https://doi.org/10.1007/s10489-018-1190-6

Gao, Z., Wei, S., Wang, L., and Fan, S., 2020, Exploring the Spatial-Temporal Characteristics of Traditional Public Bicycle Use in Yancheng, China: A Perspective of Time Series Cluster of Stations, Sustainability, Volume : 12(16), 6370. https://doi.org/10.3390/su12166370

Younan, M., Houssein, E. H., Elhoseny, M., and Ali, A. E. A., 2020, Improved Models for Time Series Cluster Representation Based Dynamic Time Warping, 2020 15th International Conference on Computer Engineering and Systems (ICCES), 1–6. https://doi.org/10.1109/ICCES51560.2020.9334608

Alexander, C., Shi, L., and Akhmametyeva, S., 2018. Using Quantum Mechanics to Cluster Time Series. https://doi.org/10.48550/arXiv.1805.01711

National Disaster Management Agency (BNPB), 2022

Shaukat, M. A., Shaukat, H. R., Qadir, Z., Munawar, H. S., Kouzani, A. Z., and Mahmud, M. A. P., 2021, Cluster Analysis and Model Comparison Using Smart Meter Data, Sensors, Volume : 21(9), 3157. https://doi.org/10.3390/s21093157

Pappu, A. R., Kar, S., and Kadu, S., 2022, ACF/PACF-Based Distance Measurement Techniques for Detection of Blockages in Impulse Lines of a Pressure Measurement Circuit for Nuclear Reactors (pp. 1017–1029). https://doi.org/10.1007/978-981-16-2761-3-89

Setiawan, I., Sumertajaya, I. M., and Afendi, F. M., 2021, Predicting and forecasting of time series models using cluster analysis, Journal of Physics: Conference Series, Volume : 1763(1), 012035. https://doi.org/10.1088/1742-6596/1763/1/012035

Anastasiou, A., Hatzopoulos, P., Karagrigoriou, A., and Mavridoglou, G., 2021, Causality Distance Measures for Multivariate Time Series with Applications, Mathematics, Volume : 9(21), 2708. https://doi.org/10.3390/math9212708

Xinyi, C., 2022, Comparison between Complete and Ward’s Linkage Method in Hierarchical Clustering Analysis on Cancer Omics Dataset. 2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB), 73–77. https://doi.org/10.1109/ICBCB55259.2022.9802487

Mattiev, J., and Kavsek, B., 2021, Distance based clustering of class association rules to build a compact, accurate and descriptive classifier, Computer Science and Information Systems, Volume : 18(3), 791–811. https://doi.org/10.2298/CSIS200430037M

Kumar, S. S., Ahmed, S. T., Vigneshwaran, P., Sandeep, H., and Singh, H. M., 2021, RETRACTED ARTICLE: Two phase cluster validation approach towards measuring cluster quality in unstructured and structured numerical datasets, Journal of Ambient Intelligence and Humanized Computing, Volume : 12(7), 7581–7594. https://doi.org/10.1007/s12652-020-02487-w

Dinh, D.-T., Fujinami, T., and Huynh, V.-N., 2019, Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient (pp. 1–17). https://doi.org/10.1007/978-981-15-1209-4-1

Jin-Heng, G., Jia-Xiang, L., Zhen-Chang, Z., and Han-Yu, L., 2022, CDBSCAN: Density clustering based on silhouette coefficient constraints, 2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), 600–605. https://doi.org/10.1109/ICCEAI55464.2022.00128

Nidheesh, N., Nazeer, K. A. A., and Ameer, P. M., 2020, A Hierarchical Clustering algorithm based on Silhouette Index for cancer subtype discovery from genomic data, Neural Computing and Applications, Volume : 32(15), 11459–11476. https://doi.org/10.1007/s00521-019-04636-5

Wang, Z., and Wang, H., 2021, Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning, IEEE Access, Volume : 9, 44770–44783. https://doi.org/10.1109/ACCESS.2021.3067060

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Published

03-02-2024

How to Cite

Imro'ah, N., & Huda, N. M. (2024). AN ANALYSIS OF CLUSTER TIMES SERIES FOR THE NUMBER OF COVID-19 CASES IN WEST JAVA. Jurnal Matematika UNAND, 12(3), 203–212. https://doi.org/10.25077/jmua.12.3.203-212.2023

Issue

Vol. 12 No. 3 (2023)

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