Pengelompokan Provinsi Berdasarkan Indikator Ekonomi, Pendidikan, Kesehatan, dan Kriminalitas di Indonesia Menggunakan Algoritma Centroid Linkage

Yossy Candra; Rito Goejantoro; Andrea Tri Rian Dani

doi:10.37905/euler.v12i1.24887

Pengelompokan Provinsi Berdasarkan Indikator Ekonomi, Pendidikan, Kesehatan, dan Kriminalitas di Indonesia Menggunakan Algoritma Centroid Linkage

Yossy Candra, Rito Goejantoro, Andrea Tri Rian Dani

Abstract

With its rich cultural diversity and abundant natural resource potential, Indonesia still faces various social science problems. Economic inequality, low quality of education, limited access to health, and high crime rates are social problems that hit various provinces in Indonesia. This research was conducted to group provinces in Indonesia based on social indicators, which include economy, education, health, and crime. This research uses cluster analysis with the Centroid Linkage algorithm to group provinces in Indonesia. The Centroid Linkage algorithm was chosen because of its advantages in producing optimal grouping. Test cluster validity using the Silhouette Coefficient (SC). The case studies used are variables that are thought to be related to economic, health, education, and crime problems in 34 provinces in Indonesia in 2021. Based on the analysis, the grouping results using the Centroid Linkage algorithm show that the optimal number of clusters is 2, with an SC value of 0.538. Cluster 1 consists of 33 provinces, and Cluster 2 consists of only one province, DKI Jakarta.

Keywords

Centroid Linkage; Cluster Analysis; Silhouette Coefficient; Social Science

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References

N. M. A. Aditia, and N. P. M. Dewi, “Pengaruh Pengeluaran Pemerintah di Bidang Pendidikan, Kesehatan, dan Ekonomi Terhadap Tingkat Kesejahteraan Masyarakat di Provinsi Bali,” Jurnal Ekonomi Pembangunan, vol. 7, no. 2, pp. 212-242, 2018. doi: https://erepo.unud.ac.id/id/eprint/19459.

D. P. Wardana, “Pengaruh Pembangunan Ekonomi Terhadap Pembangunan Manusia di Kalimantan Timur,” Jurnal Ekonomi Keuangan dan Manajemen, vol. 12, no. 2, pp. 179-191, 2016. doi: https://doi.org/10.30872/jinv.v12i2.804.

E. A. Aini, I. Isnaini, S. Sukamti, and L. N. Amalia, “Pengaruh Tingkat Pendidikan Terhadap Tingkat Kesejahteraan Masyarakat di Kelurahan Kesatrian Kota Malang,” Technomedia Journal, vol. 3, no. 1, pp. 58-72, 2018. doi: https://doi.org/10.33050/tmj.v3i1.333.

B. Tambaip, A. P. Tjilen, and Y. Ohoiwutun, “Peran Fasilitas Kesehatan untuk Kesejahteraan Masyarakat,” Jurnal Kebijakan Publik, vol. 14, no. 2, pp. 189-196, 2023. doi: http://dx.doi.org/10.31258/jkp.v14i2.8245.

Sugiharti, Lilik, M. A. Esquivas, M. S. Shaari, L. Agustin, and H. Rohmawati, “Criminality and Income Inequality in Indonesia,” Social Science, vol. 11, no. 3, pp. 142, 2022. doi:https://doi.org/10.3390/socsci11030142.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques Third Edition. Morgan Kaufman: Massachusetts (US), 2012.

B. Santoso, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis, Graha Ilmu, Yogyakarta, 2007.

A. T. R. Dani, F. B. Putra, M. Fauziyah, Sifriyani, Suyitno, and M. Fathurahman, “K-Means Algorithm For Groupibg Provinces In Indonesia Based On Macroeconomic And Criminality Indicators,” Jurnal Statistika, vol. 11, no. 2, pp. 12-21, 2023. doi:https://doi.org/10.26714/jsunimus.11.2.2023.12-21.

M. Yafi, R. Goejantoro, and A. T. R. Dani, “Pengelompokan Algoritma KMedoids dengan Principal Component Analysis (PCA),” Prosiding Seminar Nasional Matematika, Statistika, Aplikasinya, vol. 3, no. 1, pp. 183-195, 2023. ISSN 2657-232X.

Widyawati, W. L. Saptomo, and Y. R. Utami, “Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan,” Jurnal Ilmiah Sinus (JIS), vol. 18, no. 1, pp. 75-87, Jan. 2020. doi:http://dx.doi.org/10.30646/sinus.v18i1.448.

Suyanto, Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika, 2017.

F. Liantoni, and H. Nugroho, “Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier dan K-Nearest Neighboor,” Jurnal Simantec, vol. 5, no. 1, pp. 9-16, Dec. 2015. doi: https://doi.org/10.21107/simantec.v5i1.1009.

F. Nielsen, Introduction to HPC with MPI for Data Science. Springer, 2016.

Suyanti, and Y. L. Sukestiyarno, “Deteksi Outlier Menggunakan Diagnosa Regresi Berbasis Estimator Parameter Robust,” Jurnal Matematika, vol. 3, no. 2, pp. 118-125, 2014. doi: https://doi.org/10.15294/UJM.V3I2.4341.

R. Silvi, “Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokan Indikator HIV/AIDS di Indonesia,” Jurnal Matematika MANTIK, vol. 4, no. 1, pp. 22-31, 2018. doi: https://doi.org/10.15642/mantik.2018.4.1.22.

D. Dzikrullah, “Pengelompokan Provinsi Berdasarkan Kualitas Jaringan Internet dengan Metode Centroid Linkage,” Jurnal of Mathematics, Computations, and Statistics, vol. 5, no. 1, pp. 48-57, 2022. doi: https://doi.org/10.35580/jmathcos.v5i1.32363.

P. Puspita, “Perbandingan Metode Centroid dan Ward dalam Pengelompokan Tingkat Penyelesaian Pendidikan di Indonesia,” Jurnal Ilmiah Pendidikan Matematika dan Statistika, vol. 3, no. 3, pp. 501-510, 2022. doi: https://doi.org/10.46306/lb.v3i3.159.

N. A. Raja, G. M. Tinungki, and N. Sirajang, “Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan,” ESTIMASI: Journal of Statistics and Its Application, vol. 5, no. 1, pp.61–74, 2024. doi:https://doi.org/10.20956/ejsa.v5i1.13605.

I. Permana, and F. N. Salisah, “Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation,” IJIRSE, vol. 2, no. 1, pp. 67-72, 2022. doi: https://doi.org/10.57152/ijirse.v2i1.311.

A. T. R. Dani, S. Wahyuningsih, and N. A. Rizki, “Penerapan Hierachical Clustering Metode Agglomerative pada Data Runtun Waktu,” Jambura Journal of Mathematics, vol. 1, no. 2, pp. 64-78, 2019. doi: https://doi.org/10.34312/jjom.v1i2.2354.

DOI: https://doi.org/10.37905/euler.v12i1.24887