COMPARISON OF REGIONAL CLUSTER ANALYSIS ACCORDING TO INCLUSIVE DEVELOPMENT INDICATORS IN JAVA ISLAND 2018 BETWEEN HIERARCHICAL AND PARTITIONING CLUSTERING STRATEGIES
Gross Domestic Product (GDP) is one of the most common indicators to reflect a nation’s development. Indonesia's GDP has an average growth rate of 5 percent over the 2015-2019 period with the highest growth rate occurred in 2018. Furthermore, the provinces in Java Island contributed the most out of any province to Indonesia’s GDP in that year. However, the development in Java Island still has several issues, such as high poverty, unequal income distribution, and high unemployment. This problem indicates that the economic growth in Java Island has not been inclusive concerning development. This study aims to group regencies/municipalities in Java Island based on indicators of inclusive growth. These indicators refer to McKinley (2010) in a journal published by the Asian Development Bank (ADB). The cluster methods used to represent each hierarchical and partitioning are the Agglomerative Nesting (AGNES) and K-Means methods. The results of this study show that there are 3 clusters based on the AGNES method and 4 clusters based on the K-Means method. Clusters with good inclusive growth characteristics are dominated by municipality areas based on the K-Means method. Meanwhile, the clusters with low inclusive growth characteristics are dominated by regencies/municipalities on Madura Island based on the K-Means and AGNES methods. The comparison of the appropriate methods in this study based on the silhouette value is the AGNES method.
T. McKinley, “Inclusive growth criteria and indicators: an inclusive growth index for diagnosis of country progress,” Asian Dev. Bank Work. Pap., no. 14, pp. 1–34, 2010, [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Inclusive+Growth+Criteria+and+Indicators+:+An+Inclusive+Growth+Index+for+Diagnosis+of+Country+Progress#0.
M. P. Todaro and S. C. Smith, Economic Development Twelfth Edition, 12th Editi. New York: Pearson, 2014.
Badan Pusat Statistik, “Data Online Badan Pusat Statistik: Tabel Dinamis Subjek Produk Domestik Bruto,” 2019. https://www.bps.go.id/subject/11/produk-domestik-bruto--lapangan-usaha-.html#subjekViewTab3.
Kementerian Perindustrian RI, “Rencana Induk Pembangunan Industri Nasional 2015 - 2035,” Rencana Induk Pembang. Ind. Nas. 2015-2035, pp. 1–98, 2015, [Online]. Available: http://www.depkop.go.id.
I. C. Oluseye and A. A. Gabriel, “Determinants of Inclusive Growth in Nigeria : An ARDL Approach,” vol. 7, no. 3, pp. 97–109, 2017, doi: 10.5923/j.economics.20170703.01.
T. Sukwika, “Peran Pembangunan Infrastruktur terhadap Ketimpangan Ekonomi Antarwilayah di Indonesia,” J. Wil. dan Lingkung., vol. 6, no. 2, p. 115, 2018, doi: 10.14710/jwl.6.2.115-130.
Badan Pusat Statistik, “Data Online Badan Pusat Statistik: Tabel Dinamis Subjek Produk Domestik Bruto, Kemiskinan dan Ketimpangan, serta Tenaga Kerja.,” 2020, [Online]. Available: https://www.bps.go.id/subject/11/produk-domestik-bruto--lapangan-usaha-.html#subjekViewTab3.
T. Ji Long and E. Pasaribu, “Analisis Spasial Determinan Pertumbuhan Inklusif Kabupaten/Kota Di Provinsi Jawa Tengah Tahun 2017,” Semin. Nas. Off. Stat., vol. 2019, no. 1, pp. 416–423, 2020, doi: 10.34123/semnasoffstat.v2019i1.11.
S. A. Asongu, J. Nnanna, and P. N. Acha-Anyi, “Finance, inequality and inclusive education in Sub-Saharan Africa,” Econ. Anal. Policy, vol. 67, pp. 162–177, 2020, doi: 10.1016/j.eap.2020.07.006.
K. Kim, İ. İlkkaracan, and T. Kaya, “Public investment in care services in Turkey: Promoting employment & gender inclusive growth,” J. Policy Model., vol. 41, no. 6, pp. 1210–1229, 2019, doi: 10.1016/j.jpolmod.2019.05.002.
S. Tella and O. Alimi, “Determinants of Inclusive Growth in Africa: Role of Health and Demographic Changes,” African J. Econ. Rev., vol. 4, no. 2, pp. 138–146, 2016.
S. Wahyuni and Y. A. Jatmiko, “Pengelompokan Kabupaten/Kota di Pulau Jawa Berdasarkan Faktor-Faktor Kemiskinan dengan Pendekatan Average Linkage Hierarchical Clustering,” J. Apl. Stat. Komputasi Stat., vol. 10, no. 1, pp. 1–8, 2019, doi: https://doi.org/10.34123/jurnalasks.v10i1.197.
N. Ngepah, “A review of theories and evidence of inclusive growth: an economic perspective for Africa,” Curr. Opin. Environ. Sustain., vol. 24, pp. 52–57, 2017, doi: 10.1016/j.cosust.2017.01.008.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Third Edit. Amsterdam: Morgan Kauffman, 2012.
S. K. Chandel, “Intrusion Detection System using K-Means Data Mining and Outlier Detection Approach,” Masaryk University, 2017.
S. A. Alasadi and W. S. Bhaya, “Alasadi, S. A. (2017). Review of data preprocessing techniques in data mining 12(16), 4102-4107.,” J. Eng. Appl. Sci., vol. 12, no. 16, pp. 4102–4107, 2017, doi: http://dx.doi.org/10.36478/jeasci.2017.4102.4107.
H. Liu, H. Motoda, R. Setiono, and Z. Zhao, “Feature selection: An ever evolving frontier in data mining,” Featur. Sel. data Min., vol. 10, pp. 4–13, 2010, [Online]. Available: http://proceedings.mlr.press/v10/liu10b/liu10b.pdf.
C. Park and R. Claveria, “DOES REGIONAL INTEGRATION MATTER FOR INCLUSIVE GROWTH ? EVIDENCE FROM THE MULTIDIMENSIONAL REGIONAL ADB ECONOMICS Does Regional Integration Matter for Inclusive Growth ? Evidence from the Multidimensional Regional Integration Index,” ADB Econ. Work. Pap. Ser., vol. Oktober, no. 559, 2018, [Online]. Available: https://www.adb.org/sites/default/files/publication/460681/ewp-559-regional-integration-inclusive-growth.pdf.
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