USING K-MEANS FOR DISTRICT-CITY POVERTY CLUSTERING IN INDONESIA
DOI:
https://doi.org/10.25134/ilkom.v19i1.300Keywords:
Poverty, K-Means Algorithm, Within Sum of Squares (WSS, ClusteringAbstract
Poverty is one of the main challenges faced by the government in its efforts to improve people's welfare. Identifying regions based on the poverty line level is an important step to ensure well-targeted interventions. This study aims to categorize districts/cities based on poverty levels using the K-Means Algorithm, so that it can be a guide in data-based policy making. The research method starts with data collection, data selection process to handle missing values using the replacement method. Determination of the optimal number of clusters was done using Within Sum of Squares (WSS) to ensure that each region was grouped into clusters based on their level of similarity, which showed that three clusters were the ideal number. An evaluation of the clustering results was conducted to ensure the stability and accuracy of the clustering. The results show that the districts/municipalities are divided into three clusters based on the poverty line level, namely cluster 0 with a high poverty line level (241 regions), cluster 1 with a medium poverty line level (247 regions), and cluster 2 with a low poverty line level (90 regions). This study concludes that the K-Means Algorithm is effective in clustering regions based on poverty levels, providing a strong basis for data-driven decision-making. Future research is recommended to use more diverse data and cover more indicators, such as education level, access to health services, or infrastructure quality.
Downloads
References
R. Anggraini, E. Haerani, J. Jasril, and I. Afrianty, “Pengelompokkan Penyakit Pasien Menggunakan Algoritma K-Means,” Jurnal Riset Komputer, vol. 9, no. 6, p. 1840, Dec. 2022, doi: 10.30865/jurikom.v9i6.5145.
M. Faisal, N. Fajriana, and Z. Fitri, “Information and Communication Technology Competencies Clustering for students for Vocational High School Students Using K-Means Clustering Algorithm,” International Journal of Engineering, Science & InformationTechnology (IJESTY), vol. 2, pp. 111–120, 2022, doi: 10.52088/ijesty.v1i4.318.
T. Wahyudi and T. Silfia, “Implementation Of Data Mining Using K-Means Clustering Method To Determine Sales Strategy In S&R Baby Store,” Journal of Applied Engineering and Technological Science, vol. 4, no. 1, pp. 93–103, 2022.
B. Baskoro, A. Gunaryati, and A. Rubhasy, “Klasifikasi Penduduk Kurang Mampu Dengan Metode K-Means untuk Optimalisasi Program Bantuan Sosial,” Jurnal Informatika, Manajemen dan Teknologi, vol. 25, no. 1, pp. 41–48, Jun. 2023, doi: 10.23969/infomatek.v25i1.7271.
F. Handayanna and S. Sunarti, “Penerapan Algoritma K-Means Untuk Mengelompokkan Kepadatan Penduduk Di Provinsi DKI Jakarta,” Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 50–55, Mar. 2024, doi: 10.52158/jacost.v5i1.477.
S. Anwar, T. Suprapti, G. Dwilestari, and I. Ali, “Pengelompokkan Hasil Belajar Siswa Dengan Metode Clustering K-Means,” Jurnal Sistem Informasi dan Teknologi Informasi), vol. 4, no. 2, pp. 60–72, 2022.
T. Kurniawan and M. Jajuli, “Clustering Data Kecelakaan Lalu Lintas di Kecamatan Cileungsi Menggunakan Metode K-Means,” Generation Journal, vol. 6, no. 1, pp. 2580–4952, 2022.
S. Pujiono, R. Astuti, and F. M. Basysyar, “Implemetasi Data Mining Untuk Menentukan Pola Penjualan Produk Menggunakan Algoritma K-Means Clustering,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 1, pp. 615–620, 2024.
J. Jemakmun and R. A. D. S. Purboyo, “Data Clustering Recommendations For Selection Student Majors To Higher Edication Using The K-Means Method (Case Study of SMAN 2 Palembang),” Journal Of Informatics And Telecommunication Engineering, vol. 6, no. 2, pp. 367–377, Jan. 2023, doi: 10.31289/jite.v6i2.7911.
R. Rahma and R. Mufidah, “Pengelompokan Daerah Rawan Kekerasan Terhadap Perempuan Dan Anak Di Jawa Barat Menggunakan Algoritma K-Means,” Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, vol. 7, pp. 850–857, 2022.
M. R. Muttaqin, T. I. Hermanto, and M. A. Sunandar, “Penerapan K-Means Clustering Dan Cross-Industry Standard Process For Data Mining (Crisp-Dm) Untuk Mengelompokan Penjualan Kue,” Jurnal Ilmiah Ilmu Komputer dan Matematika, vol. 19, no. 1, pp. 38–53, 2022, [Online]. Available: https://journal.unpak.ac.id/index.php/komputasi
K. Haris, D. Sarjon, and S. Sumijan, “Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal,” Journal of Applied Computer Science and Technology, vol. 1, no. 2, pp. 80–89, Dec. 2020, doi: 10.52158/jacost.v1i2.102.
C. A. Sugianto and T. P. O. R. Bokings, “K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 58–67, Feb. 2021, doi: 10.47709/cnahpc.v3i1.934.
A. Supriyadi, A. Triayudi, and I. D. Sholihati, “Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas,” Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, vol. 6, pp. 229–240, 2021.
N. N. Hasanah and A. S. Purnomo, “Implementasi Data Mining Untuk Pengelompokan Buku Menggunakan Algoritma K-Means Clustering (Studi Kasus : Perpustakaan Politeknik LPP Yogyakarta),” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 4, no. 2, pp. 300–311, Jul. 2022, doi: 10.47233/jteksis.v4i2.499.
N. Nurhachita and E. S. Negara, “A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students,” Int J Comput Appl, vol. 17, no. 8, pp. 43–48, Mar. 2020, doi: 10.5120/2237-2860.
F. Sembiring, O. Octaviana, and S. Saepudin, “Implementasi Metode K-Means Dalam Pengklasteran Daerah Pungutan Liar Di Kabupaten Sukabumi (Studi Kasus : Dinas Kependudukan Dan Pencatatan Sipil),” Jurnal Tekno Insentif, vol. 14, no. 1, pp. 40–47, Apr. 2020, doi: 10.36787/jti.v14i1.165.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nuansa Informatika
This work is licensed under a Creative Commons Attribution 4.0 International License.
