Simulation Of The K-Means Clustering Algorithm With The Elbow Method in Making Clusters Of Provincial Poverty Levels in Indonesia

Joko Riyono; Christina Eni  Pujiastuti

doi:10.15642/mantik.2022.8.2.113-123

Authors

Joko Riyono Universitas Trisakti, Jakarta, Indonesia
Christina Eni Pujiastuti Universitas Trisakti, Jakarta, Indonesia

DOI:

https://doi.org/10.15642/mantik.2022.8.2.113-123

Keywords:

Budget deficit, Clustering, Optimal cluster, Poverty depth, Poverty severity

Abstract

One way to ensure that government programs and assistance for each province are right on target is to create a model of grouping or clustering provinces in Indonesia based on poverty levels. Algorithm K Means is one of the clustering methods in Data Mining to divide n observations into k groups so that each observation is in the group with the closest mean. In this study, provincial poverty level clustering in Indonesia will be made based on three poverty level indicators, namely the Percentage of Poor Population (P0), Poverty Depth (P1), and Poverty Severity (P2) with the K-Means Algorithm using the Elbow Method assisted by the Python Program. The results obtained are 5 optimal clusters of provincial poverty rates in Indonesia.

Downloads

Download data is not yet available.

References

M. Safar, "K-Means-Implementation, Problems and Related Methods"

W.Priatna, “Data Mining Lecture Module - Ubharajaya Repository.” http://repository.ubharajaya.ac.id/6318/ (accessed Aug. 31, 2022).

R. Fdillah, "Implementation of Data Mining in the Analysis of Fire Incidents in the City of Bandung Using the Association Rule Method - Elibrary Unikom." https://elibrary.unikom.ac.id/id/eprint/2686/ (accessed Aug. 31, 2022).

Nasari, “Implementation of k-means clustering on new student admission data (case study: potential university utama) Online SEMNASTEKNOMEDIA.” https://ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/837 (accessed Aug. 31, 2022).

S. Andayani, "Cluster Formation in Knowledge Discovery in Database with K-Means Algorithm - Lumbung Pustaka UNY." https://eprints.uny.ac.id/2354/ (accessed Aug. 31, 2022).

A. Asroni, H. Fitri, and E. Prasetyo, "Implementation of the Clustering Method with the K-Means Algorithm in Grouping Prospective New Student Data at Yogyakarta Muhammadiyah University (Case Study: Faculty of Medicine and Health Sciences, and Faculty of Social and Political Sciences) ,” Semesta Teknika, vol. 21, no. 1, pp. 60–64, May 2018, doi: 10.18196/ST.211211

M. Benri, H. Metisen, and S. Latipa, "Clustering analysis using the k-means method in product sales clustering at fadhila superiorities," JURNAL MEDIA INFOTAMA, vol. 11, no. 2, Sept. 2015, doi: 10.37676/JMI.V11I2.258.

Y. Darmi, A. Setiawan, J. Bali, K. Kampung Bali, K. Teluk Segara, and K. Bengkulu, "Applying the K-Means clustering method in product sales clustering," JURNAL MEDIA INFOTAMA, vol. 12, no. 2, Dec. 2016, doi: 10.37676/JMI.V12I2.418.

N. Shi, X. Liu, and Y. Guan, “Research on k-means clustering algorithm: An improved k-means clustering algorithm,” 3rd International Symposium on Intelligent Information Technology and Security Informatics, IITSI 2010, pp. 63–67, 2010, doi: 10.1109/IITSI.2010.74.

Neva Satyahadewi, “Characteristics classification with k-means cluster analysis method “ Bimaster: Scientific Bulletin of Mathematics, Statistics and Its Applications.” https://jurnal.untan.ac.id/index.php/jbmstr/article/view/3033/2998 (accessed Aug. 31, 2022).

RV.Labaree, “Quantitative Methods - Organizing Your Social Sciences Research Paper - Research Guides at University of Southern California.” https://libguides.usc.edu/writingguide/quantitative (accessed Aug. 31, 2022).

Z. Nabila, A. R. Isnain, P. Permata, and Z. Abidin, "Data mining analysis for clustering case of covid-19 in Lampung province with the K-Means algorithm," Journal of Technology and Information Systems, vol. 2, no. 2, pp. 100–108, Jul. 2021, doi: 10.33365/JTSI.V2I2.868.

"Central Bureau of Statistics." ttps://www.bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html#subjekViewTab3 (accessed Aug. 31, 2022).

E. S. Y. Pandie, “Implementation of naive bayes data mining algorithms in cooperatives,” J-ICON, vol. 6, no. 1, pp. 15–20, 2018.

T. Alfina, T. Alfina, B. Santosa, and A. R. Barakbah, "A Comparative Analysis of Hierarchical Clustering Methods, K-Means and the Combined Both in Cluster Data (Case Study: ITS Industrial Engineering Practice Work Problems)," ITS Engineering Journal, vol. 1, no. 1, pp. A521–A525, Sept. 2012, doi: 10.12962/j23373539.v1i1.1794.