Implementation of K-Means Clustering on Poverty Indicators in Indonesia
Keywords:
Poverty Gap Index, Poverty Severity Index, K-Means, Machine Learning, Cluster Analysis
Abstract
This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.
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References
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[2] B. Poerwanto, R. Y. Fa’rifah, W. Sanusi, and S. Side, “A matlab code to compute prediction of survival trends in patients with DHF,” Journal of Physics: Conference Series, vol. 1028, no. 1, pp. 1–7, Jun. 2018.
[3] M. Barchitta et al., “Cluster analysis identifies patients at risk of catheter-associated urinary tract infections in intensive care units: findings from the SPIN-UTI Network,” Journal of Hospital Infection, vol. 107, pp. 57–63, Jan. 2021.
[4] Y. Meng, J. Liang, F. Cao, and Y. He, “A new distance with derivative information for functional k-means clustering algorithm,” Information Sciences, vol. 463–464, pp. 166–185, Oct. 2018.
[5] S. Askari, “Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development,” in Expert Systems with Applications, Mar. 2021, vol. 165, pp. 1–71.
[6] B. Poerwanto and R. Y. Fa’rifah, “Analisis Cluster K-Means dalam Pengelompokan Kemampuan Mahasiswa,” Indonesian Journal of Fundamental Sciences, vol. 2, no. 2, pp. 92–96, 2016.
[7] J. Heil, V. Häring, B. Marschner, and B. Stumpe, “Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils,” Geoderma, vol. 337, pp. 11–21, Mar. 2019.
[8] V. Heidrich-Meisner and R. F. Wimmer-Schweingruber, Solar wind classification via k-means clustering algorithm. Elsevier Inc., 2018.
[9] B. Poerwanto, “Evaluating the K-Means Analysis in Clustering Area Based on Estates Productivity in Tana Luwu Using Silhouette Index,” Journal of Physics: Conference Series, vol. 1752, no. 1, pp. 1–7, Feb. 2021.
[10] M. Moatsos and A. Lazopoulos, “Global poverty: A first estimation of its uncertainty,” World Development Perspectives, vol. 22, pp. 1–16, Jun. 2021.
[11] BPS, Berita Resmi Statistik. Jakarta: Badan Pusat Statistika, 2020.
[12] A. T. Rahman, Wiranto, and A. Rini, “Coal Trade Data Clustering Using K-Means (Case Study Pt. Global Bangkit Utama),” ITSMART: Jurnal Teknologi dan Informasi, vol. 6, no. 1, pp. 24–31, 2017.
[13] Y. Hozumi, R. Wang, C. Yin, and G.-W. Wei, “UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets,” Computers in Biology and Medicine, vol. 131, pp. 1–14, Apr. 2021.
[14] M. A. Nahdliyah, T. Widiharih, and A. Prahutama, “Metode K-Medoids Clustering dengan Validasi Silhouette Index dan C-Index,” Jurnal Gaussian, vol. 8, no. 2, pp. 161–170, 2019.
[15] N. Chalid and Y. Yusuf, “Pengaruh Tingkat Kemiskinan dan Tingkat Pengangguran, Upah Minimun Kabupaten/Kota Dan Laju Pertumbuhan Ekonomi Terhadap Indeks Pembangunan Manusia di Provinsi Riau,” Jurnal Ekonomi, vol. 22, no. 2, pp. 1–12, 2014.
[16] A. Artino, B. Juanda, and S. Mulatsih, “The Relationship of Village Funds to Poverty,” Tataloka, vol. 21, no. 3, pp. 381–389, 2019.
Published
2022-03-31
How to Cite
Annas, S., Poerwanto, B., Sapriani, S., & S, M. (2022). Implementation of K-Means Clustering on Poverty Indicators in Indonesia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 257-266. https://doi.org/https://doi.org/10.30812/matrik.v21i2.1289
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