Fuzzy C-Means for Regional Clustering in East Java Province Based on Human Development Index Indicators

Marita Qori'atunnadyah

doi:10.36456/jstat.vol16.no2.a8240

Authors

Marita Qori'atunnadyah Institut Teknologi dan Bisnis Widya Gama Lumajang

DOI:

https://doi.org/10.36456/jstat.vol16.no2.a8240

Keywords:

Cluster, Fuzzy C-Means, HDI, Indicator

Abstract

The Human Development Index (HDI) is the UN's key metric for gauging human advancement within a country, blending vital elements like per capita income, life expectancy, and education. In Indonesia, the HDI assesses societal well-being, with East Java's HDI lagging behind national and governmental targets despite mitigation efforts. To address this, the study utilizes Fuzzy C-Means clustering to classify East Java's regions based on HDI indicators, revealing five optimal groups via pseudo-F-statistic analysis. One-way MANOVA confirms variations among these groups, while One-Way ANOVA validates the significance of the four HDI indicators in categorization. The HDI-based categorization denotes Group 3 as high-status, Group 1 as low-status, Group 2 as moderately high-status, Group 4 as moderate, and Group 5 as moderately low-status. Consequently, it's advised that the government concentrates on improving low-HDI groups to uplift East Java's populace. This research can serve as a cornerstone for policymakers and stakeholders in their efforts to enhance the HDI in this region.

References

Badan Pusat Statistik, “Statistik Indonesia,” Jakarta, 2019.

Kementerian PPN/Bappenas, “Pemuktahiran Rencana Kerja Pemerintah (RKP) Tahun 2022,” 2021. Accessed: Mar. 29, 2023. [Online]. Available: https://www.bappenas.go.id/show-result-satudata?name=publikasi&key=rkp&tahun=

Badan Pusat Statistik, “ Indeks Pembangunan Manusia menurut Provinsi 2020-2022,” 2023.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, “MULTIVARIATE DATA ANALYSIS EIGHTH EDITION,” 2019. [Online]. Available: www.cengage.com/highered

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis 6th Edition, 6th ed. United States of America: Pearson Prentice Hall, 2007.

Y. Agusta, “K-Means-Penerapan, Permasalahan dan Metode Terkait,” 2007.

S. A. Mingoti and J. O. Lima, “Comparing SOM neural network with Fuzzy c-means, K-means, and traditional hierarchical clustering algorithms,” Eur J Oper Res, vol. 174, no. 3, pp. 1742–1759, Nov. 2006, doi: 10.1016/j.ejor.2005.03.039. DOI: https://doi.org/10.1016/j.ejor.2005.03.039

M. Qori’atunnadyah and F. D. Rahmawati, “Pengelompokkan Kabupaten dan Kota Berdasarkan Kondisi Infrastruktur Jalan Menggunakan Hierarchical Clustering,” Journal of Informatics Development, vol. 1, no. 1, pp. 1–5, 2022. DOI: https://doi.org/10.30741/jid.v1i1.894

M. Qori’atunnadyah, “Pengelompokkan Wilayah Berdasarkan Rasio Guru-Murid Pada Jenjang Pendidikan Menggunakan Algoritma K-Means,” Journal of Informatics Development, vol. 1, no. 2, pp. 33–38, 2022. DOI: https://doi.org/10.30741/jid.v1i1.898

F. Idris, F. Azmi, and P. S. Daru Kusuma, “PENGELOMPOKAN DATA GURU DI INDONESIA MENGGUNAKAN K-MEANS CLUSTERING TEACHER DATA GROUPING IN INDONESIA USING K-MEANS CLUSTERING,” eProceedings of Engineering, vol. 6, no. 2, pp. 5648–5653, 2019.

M. Qori’atunnadyah, “Metode C-Means untuk Pengelompokkan Kabupaten/Kota Provinsi Jawa Timur berdasarkan Indikator Indeks Pembangunan Manusia (IPM),” Journal of Informatics Development, vol. 1, no. 2, pp. 51–58, 2023, doi: 10.30741/jid.v2i2.1013. DOI: https://doi.org/10.30741/jid.v2i2.1013

H. A. Chusna and A. T. Rumiati, “Penerapan Metode K-Means dan Fuzzy C-Means untuk Pengelompoan Sekolah Menengah Pertama (SMP) di Indonesia Berdasarkan Standar Nasional Pendidikan (SNP),” Jurnal Sains dan Seni ITS, vol. 9, no. 2, Feb. 2021, doi: 10.12962/j23373520.v9i2.58349. DOI: https://doi.org/10.12962/j23373520.v9i2.58349

N. Dwitiyanti, N. Selvia, and F. R. Andrari, “Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia Menurut Indikator Kesejahteraan Rakyat,” Faktor Exacta, vol. 12, no. 3, p. 201, Nov. 2019, doi: 10.30998/faktorexacta.v12i3.4526. DOI: https://doi.org/10.30998/faktorexacta.v12i3.4526

G. S. Nugraha and B. A. Riyandari, “IMPLEMENTASI FUZZY C-MEANS UNTUK PENGELOMPOKAN DAERAH BERDASARKAN INDIKATOR KESEHATAN,” Jurnal Teknologi Informasi, vol. 4, no. 1, pp. 52–62, Jun. 2020, doi: 10.36294/jurti.v4i1.1222. DOI: https://doi.org/10.36294/jurti.v4i1.1222

I. Irwan, S. Sidjara, and A. P. Aryati, “Pengelompokan Jenis Penerimaan Pajak di Kota Makassar Menggunakan Fuzzy Clustering,” Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi, vol. 10, no. 1, pp. 98–102, May 2022, doi: 10.34312/euler.v10i1.14225. DOI: https://doi.org/10.34312/euler.v10i1.14225

S. F. Octavia and M. Mustakim, “Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 2, pp. 88–94, Sep. 2021, doi: 10.47065/bits.v3i2.1005. DOI: https://doi.org/10.47065/bits.v3i2.1005

BPS-Statistics of Jawa Timur Province, “Provinsi Jawa Timur Dalam Angka 2023,” Surabaya, 2023.

T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Commun Stat Theory Methods, vol. 3, no. 1, pp. 1–27, 1974, doi: 10.1080/03610927408827101. DOI: https://doi.org/10.1080/03610927408827101

T. Hochin et al., “Quasi-optimality under pseudo F statistic in clustering data Quasi-optimality under pseudo F statistic in clustering data Quasi-optimality under pseudo f statistic in clustering data,” 2018. [Online]. Available: www.sciencepubco.com/index.php/IJET DOI: https://doi.org/10.14419/ijet.v7i2.28.13205

A. C. Rencher and W. F. Christensen, Methods of Multivariate Analysis, 3rd ed. Wiley, 2012. DOI: https://doi.org/10.1002/9781118391686

S. Askari, “Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development,” Expert Syst Appl, vol. 165, p. 113856, Mar. 2021, doi: 10.1016/j.eswa.2020.113856. DOI: https://doi.org/10.1016/j.eswa.2020.113856