A District/City Profiling Based on Poverty Indicators in East Nusa Tenggara Using the Centroid Linkage Algorithm
DOI:
https://doi.org/10.31102/zeta.2025.10.2.81-91Keywords:
Cluster Analysis, Centroid Linkage, Poverty, Squared EuclideanAbstract
Poverty is a complex multidimensional phenomenon that significantly impacts human life. Poverty has always been a problem that the government has discussed regionally, centrally, and internationally. The issue of poverty is interesting to approach and analyze using a statistical approach, namely cluster analysis. Cluster analysis is used to group objects based on their level of similarity. In this research, the algorithm used is the Centroid Linkage Algorithm. The Centroid Linkage algorithm was chosen based on its advantages in the grouping process. Distance similarity measurement uses Squared Euclidean. The data used are district/city poverty indicators in East Nusa Tenggara Province. The analysis results show that two optimal clusters were obtained with their distinguishing characteristics. Hopefully, the results of this analysis can be used as a reference in formulating policies for alleviating poverty.
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Aditya, A., Sari, B. N., & Padilah, T. N. (2021). Perbandingan pengukuran jarak Euclidean dan Gower pada klaster k-medoids. Jurnal Teknologi dan Sistem Komputer, 9(1), 1–7.
Doi: 10.14710/jtsiskom.2021.13747
Dewi, I., Syaripuddin, & Hayati, M. N. (2020). Pengelompokan Data Kategorik Dengan Algoritma Robust Clustering Using Links (Studi Kasus: PT. Prudential Life Jalan MT. Haryono Samarinda). Jurnal EKSPONENSIAL, 11(2), 133–138.
https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/655
Dzikrullah, A. A. (2022). Pengelompokan Provinsi Berdasarkan Kualitas Jaringan Internet Dengan Metode Centroid Linkage. Journal of Mathematics, Computations, and Statistics, 5(1), 48–57.
https://doi.org/10.35580/jmathcos.v5i1.32363
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014) Multivariate Data Analysis 7th Edition, Person Education, Inc, USA.
Han, J., Kamber, M., & Pei, J. (2012) Data Mining Concepts and Techniques Third Edition, Elsevier, USA.
Handoyo, R., Mangkudjaja, R. & Nasution, S. M. (2014). Perbandingan Metode Clustering Menggunakan Metode Single Linkage dan K-Means pada Pengelompokan Dokumen. Jurnal SIFO Mikrosil, 15(2), 73–82, 2014.
https://doi.org/10.55601/jsm.v15i2.161
Hikmah, Fardinah, Qadrini, L., & Tande, E. (2022). Analisis Klaster Pengelompokan Kecamatan di Sulawesi Barat Berdasarkan Indikator Pendidikan. Jurnal Matematika, Sains, dan Pembelajarannya, 8(2), 188–196.
Doi: 10.31605/saintifik.v8i2.383
Karlita, T. (2006) Algoritma Perbaikan Penentuan Titik Pusat Awal Berbasis Hirarki untuk Klasterisasi Data Kategorikal, Institut Teknologi Sepuluh Nopember, Surabaya.
Mohibullah, M., Hossain, M. Z., & Hasan, M. (2015). Comparison of Euclidean Distance Function and Manhattan Distance Function Using K-Medoids. International Journal of Computer Science and Information Security, 13(10), 61–71.
Mongi, C. E. (2015). Penggunaan Analisis Two Step Clustering untuk Data Campuran. d’Cartesian, 4(1), 9–19.
https://doi.org/10.35799/dc.4.1.2015.7251
Nanang, A. & Susanti, D. (2018). Penerapan Data Mining Menggunakan Algoritma K-Means untuk Mengelompokkan Konsumen pada CV Mizan Cirebon. Prosiding Seminar Nasional Energi & Teknologi (SINERGI), 3, 93–102.
https://jurnal.unismabekasi.ac.id/index.php/sinergi/article/view/1148/1016
Prasetyo, E. (2012) Data mining : konsep dan aplikasi menggunakan MATLAB, CV Andi Offset, Yogyakarta.
Raja, N. A., Tinungki, G. M., & Sirajang, N. (2024). Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan. ESTIMASI: Journal of Statistics and Its Application, 5(1), 61–74.
https://doi.org/10.20956/ejsa.v5i1.13605
Santosa, B. (2007) Data Mining, Teknik Pemanfaatan Data untuk Keperluan Bisnis : Teori dan Aplikasi, Graha Ilmu, Yogyakarta.
Shoolihah, A., Furqon, M. T., & Widodo, A. W. (2017). Implementasi Metode Improved K-Means untuk Mengelompokkan Titik Panas Bumi. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1(11), 1270–1276.
https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/453
Silvi, R. (2018). Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokan Indikator HIV/AIDS di Indonesia. Jurnal Matematika MANTIK, 4(1), 22–31.
https://doi.org/10.15642/mantik.2018.4.1.22-31
Simamora, B. (2005) Analisis Multivariat Pemasaran, Gramedia Pustaka Utama, Jakarta.
Sokal, R. R. & Michener, C. D. (1958). A Statistical Method for Evaluating Systematic Relationship. University of Kansas Scientific Bulletin, 28, 1409–1438.
Tan, P. N., Steinbach, M., & Kumar, V. (2006) Introduction to Data Mining, Person Education, Inc, USA.
Widodo, E., Sari, N. N., Hidayati, I., Yubinas, F., Yuniarti, M., & Novyantika, R. D. (2018). Analisis Cluster Penderita Disabilitas Mental di Provinsi Daerah Istimewa Yogyakarta Tahun 2016. Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya (KNPMP) III, 577–586.
http://hdl.handle.net/11617/10140
Xu, N., Xu, C., Finkelman, R. B., Engle, M. A., Li, Q., Peng, M., He, L., Huang, B., & Yang Y. (2022). Coal elemental (compositional) data analysis with hierarchical clustering algorithms. International Journal of Coal Geology, 249.
