Clustering Regency and City in East Java Based on Population Density and Cumulative Confirmed COVID-19 Cases

Authors

  • Khusnia Nurul Khikmah Surabaya State University
  • A'yunin Sofro Surabaya State University

DOI:

https://doi.org/10.21512/comtech.v12i2.6891

Keywords:

clustering, population density, confirmed cases, COVID-19

Abstract

Coronavirus is a big family of viruses that causes acute respiratory syndrome and mediates human-to-human by the environment. A factor that affects the spread of infectious diseases is population density. Therefore, it is necessary to study the effect of population density on infectious diseases like COVID-19. The research analyzed the effect of the population density of each regency in East Java on cumulative confirmed COVID-19 cases until December 9, 2020. The research applied quantitative method using the agglomerative hierarchical clustering method. The clustering method included single, average, and complete linkages. The results of clustering using single linkage and average linkages have the same results for the population density of Jember Regency. This regency has the lowest effect for the cumulative confirmed COVID-19 cases. Then, complete linkage obtains that Banyuwangi Regency and Gresik Regency has the population density with the lowest effect for the cumulative number of confirmed COVID-19 cases. The results of clustering with single, average, and complete linkages have the same results for population density with a big effect on the cumulative number of confirmed COVID-19 cases in Surabaya City. The results of best clustering regencies or cities that population density affects the number of

Dimensions

Plum Analytics

Author Biographies

Khusnia Nurul Khikmah, Surabaya State University

Mathematics Department, Faculty of Mathematics and Natural Sciences

A'yunin Sofro, Surabaya State University

Mathematics Department, Faculty of Mathematics and Natural Sciences

References

Abboud, A., Cohen-Addad, V., & Houdrougé, H. (2019). Subquadratic high-dimensional hierarchical clustering. Advances in Neural Information Processing Systems, 32, 11580-11590.

Ali, P. J. M., & Faraj, R. H. (2014). Data normalization and standardization: A technical report. Machine Learning Technical Reports, 1(1), 1-6.

Badan Pusat Statistik. (2020). Beranda. Retrieved from https://www.bps.go.id/

Balcan, M. F., & White, C. (2017). Clustering under local stability: Bridging the gap between worst-case and beyond worst-case analysis. arXiv preprint arXiv:1705.07157.

Bateni, M. H., Behnezhad, S., Derakhshan, M., Hajiaghayi, M. T., Kiveris, R., Lattanzi, S., & Mirrokni, V. (2017). Affinity clustering: Hierarchical clustering at scale. In 31st Conference on Neural Information Processing Systems (NIPS 2017) (pp. 1-11).

Charikar, M., Chatziafratis, V., Niazadeh, R., & Yaroslavtsev, G. (2019). Hierarchical clustering for euclidean data. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2721-2730). PMLR.

Cheng, H. Y., Jian, S. W., Liu, D. P., Ng, T. C., Huang, W. T., & Lin, H. H. (2020). Contact tracing assessment of COVID-19 transmission dynamics in Taiwan and risk at different exposure periods before and after symptom onset. JAMA internal medicine, 180(9), 1156-1163.

Chuan, Z. L., Ismail, N., Shinyie, W. L., Ken, T. L., Fam, S. F., Senawi, A., & Yusoff, W. N. S. W. (2018). The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments. IOP Conference Series: Materials Science and Engineering, 342, 1-10.

Cohen-Addad, V., Kanade, V., Mallmann-Trenn, F., & Mathieu, C. (2019). Hierarchical clustering: Objective functions and algorithms. Journal of the ACM (JACM), 66(4), 1-42.

Fahim, A. (2017). A clustering algorithm based on local density of points. International Journal of Modern Education and Computer Science, 9(12), 9-16.

Fairuzi, N., & Hamidah, H. P. (2016). Analisis hubungan kekerabatan curcuma spp. berdasarkan karakter morfologi dan metabolit sekunder (Thesis). Universitas Airlangga.

Felsenstein, J. (2004). Inferring phylogenies. Sinauer Associates, Inc.

Ghebreslassie, B. M., Githiri, S. M., Mehari, T., & Kasili, R. W. (2015). Analysis of diversity among potato accessions grown in Eritrea using single linkage clustering. American Journal of Plant Sciences, 6, 2122-2127.

Ghoshdastidar, D., Perrot, M., & Von Luxburg, U. (2019). Foundations of comparison-based hierarchical clustering. Advances in Neural Information Processing Systems, 32, 7456-7466.

Govender, P., & Sivakumar, V. (2020). Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research, 11(1), 40-56.

Grosswendt, A., & Roeglin, H. (2017). Improved analysis of complete-linkage clustering. Algorithmica, 78(4), 1131-1150.

Hartini, E. (2014). Metode clustering hirarki. Pusat Pengembangan Teknologi Informasi dan Komputasi BATAN.

Hui, D. S., Azhar, E. I., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., ... & Petersen, E. (2020). The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases, 91, 264-266.

Kementerian Kesehatan Republik Indonesia. (2020). FAQ. https://www.kemkes.go.id/folder/view/full-content/structure-faq.html

Kusuma, A. P., & Sukendra, D. M. (2016). Analisis spasial kejadian demam berdarah dengue berdasarkan kepadatan penduduk. Unnes Journal of Public Health, 5(1), 48-56.

Majerova, I., & Nevima, J. (2017). The measurement of human development using the Ward method of cluster analysis. Journal of International Studies, 10(2), 239-257.

Merler, S., & Ajelli, M. (2010). The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proceedings of the Royal Society B: Biological Sciences, 277(1681), 557-565.

Moseley, B., & Wang, J. R. (2017). Approximation bounds for hierarchical clustering: Average linkage, bisecting k-means, and local search. In Proceedings of the 31st International Conference on Neural Information Processing Systems.

Mu'afa, S. F., & Ulinnuha, N. (2019). Perbandingan metode single linkage, complete linkage dan average linkage dalam pengelompokan kecamatan berdasarkan variabel jenis ternak Kabupaten Sidoarjo. Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, 4(2), 1-5.

Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97.

Neiderud, C. J. (2015). How urbanization affects the epidemiology of emerging infectious diseases. Infection Ecology & Epidemiology, 5(1), 1-9.

Nelwan, J. E. (2020). Kejadian Corona Virus Disease 2019 berdasarkan kepadatan penduduk dan ketinggian tempat per wilayah kecamatan. Indonesian Journal of Public Health and Community Medicine, 1(2), 039-045.

Nishom, M. (2019). Perbandingan akurasi Euclidean distance, Minkowski distance, dan Manhattan distance pada algoritma K-means clustering berbasis chi-square. Jurnal Informatika, 04(01), 20-24.

Nugroho, S. (2008). Statistika multivariat terapan. Bengkulu: UNIB Press.

Ros, F., & Guillaume, S. (2019). A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise. Expert Systems with Applications, 128, 96-108.

Roux, M. (2018). A comparative study of divisive and agglomerative hierarchical clustering algorithms. Journal of Classification, 35(2), 345-366.

Roy, A., & Pokutta, S. (2017). Hierarchical clustering via spreading metrics. The Journal of Machine Learning Research, 18(1), 3077-3111.

Sajadi, M. M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles-Wilhelm, F., & Amoroso, A. (2020). Temperature, humidity, and latitude analysis to estimate potential spread and seasonality of coronavirus disease 2019 (COVID-19). JAMA network open, 3(6), 1-11.

Satuan Tugas Penanganan COVID-19. (2020). Beranda. https://covid19.go.id

Setiawan, B., Djanali, S., & Ahmad, T. (2017). A study on intrusion detection using centroid-based classification. Procedia Computer Science, 124, 672-681.

Sihombing, G. F., Marsaulina, I., & Ashar, T. (2014). Hubungan curah hujan, suhu udara, kelembaban udara, kepadatan penduduk dan luas lahan pemukiman dengan kejadian demam berdarah dengue di Kota Malang periode tahun 2002-2011. Lingkungan dan Keselamatan Kerja, 3(1), 1-9.

Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy: The principles and practice of numerical classification. W H Freeman & Co.

Triayudi, A., & Fitri, I. (2019). A new agglomerative hierarchical clustering to model student activity in online learning. Telkomnika, 17(3), 1226-1235.

Wang, Y., Qin, K., Chen, Y., & Zhao, P. (2018). Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi GPS data. ISPRS International Journal of Geo-Information, 7(1), 1-20.

Wu, Z., & McGoogan, J. M. (2020). Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA, 323(13), 1239-1242.

Yaroslavtsev, G., & Vadapalli, A. (2018). Massively parallel algorithms and hardness for single-linkage clustering under ℓp-distances. In 35th International Conference on Machine Learning (ICML'18).

Downloads

Published

2021-11-17

Issue

Section

Articles
Abstract 459  .
PDF downloaded 502  .