The Clustering of Provinces in Indonesia by The Economic Impact of Covid-19 using Cluster Analysis
Pengelompokkan Provinsi di Indonesia dengan Ekonomi Terdampak Covid-19 Menggunakan Analisis Cluster
DOI:
https://doi.org/10.29244/ijsa.v5i1p117-129Keywords:
Kebijakan, Ekonomi, Covid-19, Analisis ClusterAbstract
The Covid-19 pandemic has hit Indonesia since March 2020. Several policies have been issued by the Indonesian government to reduce the level of the spread of Covid-19. This policy has an impact on various fields of life, especially the economic sector in various sectors. This study was conducted to analyze the grouping of provinces whose economies are at risk of being affected by Covid-19 based on various economic sectors, namely the unemployment rate, the percentage of poor people, the provincial minimum wage, and the occupancy rate of hotels using cluster analysis. Cluster analysis was performed using several hierarchical methods, namely Simple, Complete, Average, and Centroid Linkage and Ward. The Cophenetic correlation coefficient (rCoph) was used to determine the best method, while the number of clusters was determined based on the Dunn, Connectivity, and Silhoutte indexes. The analysis result shows that Average Linkage is the best method with two clusters. The first cluster consists of all provinces in Indonesia except Papua, whose economy is highly at risk of being affected by Covid-19, characterized by a low percentage of the poor and a low provincial minimum wage, as well as high levels of open unemployment and hotel occupancy rates. Meanwhile, the second cluster consists of the Province of Papua, which is an economic group with a low risk of being affected by Covid-19. By looking at the impact of the Covid-19 disaster, the government can make recovery efforts and generalize economic recovery policies due to Covid-19 which have an impact on the economy of almost all provinces in Indonesia.
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References
Hair, Joseph. F., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis (7th ed.). Pearson Education, Inc.
Halim, N. N., & Widodo, E. (2017). Clustering Dampak Gempa Bumi di Indonesia Menggunakan Kohonen Self Organizing Maps. Prosiding Seminar Nasional Integrasi Matematika dan Nilai Islami, 1(1), 188–194.
Hardilawati, W. L. (2020). Strategi Bertahan UMKM di Tengah Pandemi Covid-19. Jurnal Akuntansi & Ekonomika, 10(1), 89–98.
Johnson, Richard. A., & Wichern, Dean. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson Education, Inc.
Kemlu. (2020). Kedutaan Besar Republik Indonesia di BRUSSELS, Merangkap Luksemburg & Uni Eropa BELGIA. Kementerian Luar Negeri Republik Indonesia. https://kemlu.go.id/brussels/id
Kovács, F., Legány, C., & Babos, A. (2005). Cluster Validity Measurement Techniques. Jurnal 6th International Symposium of Hungarian Researchers on Computational Intelligence, 11.
Kurniawansyah, H., Amrullah, Salahuddin, M., Muslim, & Nurhidayati, S. (2020). Konsep Kebijakan Strategis dalam Menangani Eksternalitas Ekonomi dari Covid-19 pada Masyarakat Rentan di Indonesia. Indonesian Journal of Social Sciences and Humanities, 1(2), 130–139.
Mattjik, A. A., & Sumertajaya, I. M. (2011). Sidik Peubah Ganda. IPB Press. Bogor., 423.
Our World in Data. (2020). Coronavirus Pandemic (COVID-19) – the data. https://ourworldindata.org/coronavirus-data
Stevens, H. (2020, Maret). Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve.†Washington Post. https://www.washingtonpost.com/graphics/2020/world/corona-simulator/?fbclid=IwAR0KWReI9vtrX0BvzEssTdr3GuhEuytyt42HPrF8BjOJGiVNdc0s9rFcn4U&utm_source=reddit.com
Suparman, N., Sakti, F. T., & Anwar, H. S. (2020). Covid-19: Kebijakan Mitigasi Penyebaran dan Dampak Sosial Ekonomi di Indonesia. 13.
Syafrida, & Hartati, R. (2020). Bersama Melawan Virus Covid 19 di Indonesia. Jurnal Sosial dan Budaya Syar’i, 7(6), 495–508. https://doi.org/10.15408/sjsbs.v7i6.15325
Ulya, H. N. (2020). Alternatif Strategi Penanganan Dampak Ekonomi Covid-19 Pemerintah Daerah Jawa Timur pada Kawasan Agropolitan. Journal of Islamic Economic and Bussiness, 03(01), 80–109.
