Pengelompokan Kabupaten dan Kota di Jawa Timur berdasarkan Percepatan Pemulihan Ekonomi Menggunakan Pendekatan Hirearki

Arinda Mahadesyawardani; Azizah Atsariyyah Zhafirab; Jovansha Ariyawan; Edla Putri Humaira; M. Fariz Fadillah Mardianto; Dita Amelia; Elly Ana

doi:10.30872/eksponensial.v15i1.1273

Arinda Mahadesyawardani Universitas Airlangga
Azizah Atsariyyah Zhafirab Universitas Airlangga
Jovansha Ariyawan Universitas Airlangga
Edla Putri Humaira Universitas Airlangga
M. Fariz Fadillah Mardianto Universitas Airlangga
Dita Amelia Universitas Airlangga
Elly Ana Universitas Airlangga

DOI: https://doi.org/10.30872/eksponensial.v15i1.1273

Abstract

The Covid-19 pandemic's diverse impact on Indonesia's economy, particularly in East Java, spurred the government to formulate a comprehensive work plan targeting three key objectives, one of which is to expedite economic recovery. This plan focuses on three key indicators: economic growth, open unemployment rate (TPT), and the gini ratio. It is known that during the pandemic, East Java initially experienced economic growth that contracted until eventually showing positive growth in the second quarter of 2021, which has been supported by national policies. This study explores district and city classification in East Java based on economic recovery indicators through hierarchical clustering. The analysis identifies Ward's linkage as the most effective model, with a cophenetic correlation coefficient of 0.9311. Internal clustering validation tests reveal two optimal clusters. Cluster 1 is characterized by a notably high average acceleration of economic recovery across all three indicators. The findings suggest that the government should optimize the economic stimulus program for cluster 2 and focus on enhancing income redistribution and job opportunities for cluster 1.

Downloads

Download data is not yet available.

References

Afifi, A., May, S., Donatello, R., & Clark, V.A. (2019). Practical Multivariate Analysis (6th ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315203737.
Aini, F. N., Palgunadi, S., & Anggrainingsih, R. (2014). Clustering Business Process Model Petri Net. Jurnal Itsmart, 3(2), 47–51.
Bezdek, J. C., & Pal, N. R. (1995). Cluster validation with generalized Dunn’s indices. Proceedings - 1995 2nd New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, ANNES 1995, March, 190–193. https://doi.org/10.1109/ANNES.1995.499469
Badan Pusat Statistik Jawa Timur. (2023). Tingkat Pengangguran Terbuka (TPT) Menurut Kabupaten/Kota (Persen), 2021-2023. Jawa Timur: Badan Pusat Statistik.
Badan Pusat Statistik Jawa Timur. (2022). Gini Rasio Menurut Kabupaten/Kota di Jawa Timur 2020-2022. Jawa Timur: Badan Pusat Statistik.
Badan Pusat Statistik Jawa Timur. (2022). Pertumbuhan Ekonomi Menurut Kabupaten/Kota (Persen) 2018-2022. Jawa Timur: Badan Pusat Statistik.
Da Silva, A. R., & Dias, C. T. dos S. (2013). A cophenetic correlation coefficient for tocher’s method. Pesquisa Agropecuaria Brasileira, 48(6), 589–596. https://doi.org/10.1590/S0100-204X2013000600003.
Hair, J., Anderson, R., Babin, B., & Black, W. (2010). Multivariate Data Analysis.pdf. In Australia : Cengage: Vol. 7 edition (p. 758).
Halim, N. N., & Widodo, E. (2017). Clustering Dampak Gempa Bumi di Indonesia Menggunakan Kohonen Self Organizing Maps (SOM). Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika Dan Nilai-Nilai Islami), 1(1): 188–194.
Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis.: Pearson Prentice Hall. In Pearson Prentice Hall.
Naes, T., Isaksson, T., Fearn, T., & Davies, T. (2002). Multivariate calibration and classification. West Sussex: NIR Publication.
Nahdliyah, M. A., Widiharih, T., & Prahutama, A. (2019). Metode K-Medoids Clustering dengan Validasi Silhouette Index dan C-Index (Studi Kasus Jumlah Kriminalitas Kabupaten dan kota di Jawa Tengah Tahun 2018). Jurnal Gaussian, 8(2), 161-170. https://doi.org/10.14710/j.gauss.8.2.161-170.
Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi Offset.
Saracli, S., Dogan, N., dan Dogan, I.. (2013). Comparison of Hierarchical Cluster Analysis Methods by Cophenetic Correlation. Journal of Inequalities and Applications, Hal. 203. https://doi.org/10.1186/1029-242X-2013-203.
Widarjono, A. (2010). Analisis Statistika Multivariat Terapan. Edisi pertama. Yogyakarta: UPP STIM YKPN
Wierzchoń, S. T., & Kłopotek, M. A. (2018). Cluster Analysis. In Studies in Big Data (Vol. 34). https://doi.org/10.1007/978-3-319-69308-8_2.