Clustering Data Penduduk Menggunakan Algoritma K-Means
- Tomi Ikhsan Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Elin Haerani
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Fitri Wulandari Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Economic inequality is still a crucial factor facing Indonesia today, from big cities to remote villages, economic inequality is still a major problem. Bina Baru Village is no exception, a village inhabited by 5,760 people with a total of 1,742 families, spread across 30 neighbourhood associations (RT) and 8 community associations (RW). Various efforts are made to overcome the problem of economic inequality, one of which is by channeling assistance or providing policies that are right on target. One of the steps to overcome this problem is to group population data in Bina Baru village using the K-Means Clustering method which aims to determine the economic level of families in the region, so that local governments can more accurately make policies on the problem of economic inequality that occurs. The data used comes from a questionnaire of 1,005 family data with 64 attributes and 1,005 individual data with 84 attributes. The application of the k-means algorithm is carried out using python, also using DBI (Davies-Bouldin Index) to determine the optimum k value. In this study, the optimal k value is 3 clusters. Based on testing, it is found that Cluster 0 represents households with medium economic conditions, cluster 1 represents groups with better economic conditions and Cluster 2 is a group of households with low economic conditions. By clustering the population's economy, it is hoped that it can help stakeholders to provide targeted policies.
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References
Amelia, F., Iskandar, I., Kurnia Gusti, S., & Haerani, E. (2023). Clustering Keluarga Miskin Desa Bina Baru Dengan Metode K-Medoids. Krea-Tif: Jurnal Teknik Informatika, 11(1), 1–13. https://doi.org/10.32832/krea-tif.v11i1.14104
Bahtiar, D. (2023). Pemetaan Penduduk Penerima Bantuan Sosial Desa Waru Jaya Menggunakan Algoritma K-Means Clustering. Scientia Sacra: Jurnal Sains, 3(2), 29–39. https://doi.org/https://doi.org/10.31328/jointecs.v4i1.998
Bustami, B., Mahara, R., Ahmadian, H., Wahyuni, S., & AR, K. (2022). Analisis Clustering Penduduk Miskin Di Provinsi Aceh Menggunakan Algoritma K-Means Dan X-Means. Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI), 5(1), 26–35. https://doi.org/10.32672/jnkti.v5i1.3961
Farissa, R. A., Mayasari, R., & Umaidah, Y. (2021). Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung. Journal of Applied Informatics and Computing, 5(2), 109–116. https://doi.org/10.30871/jaic.v5i1.3237
Febriansyah, F., & Muntari, S. (2023). Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam. Jurnal Informatika Sunan Kalijaga, 8(1), 66–77. https://doi.org/https://doi.org/10.14421/jiska.2023.8.1.66-77
Fitriyadi, A. upi. (2021). Analisis Algoritma K-Means dan K-Medoids Untuk Clustering Data Kinerja Karyawan Pada Perusahaan Perumahan Nasional. Kilat, 10(1), 157–168. https://doi.org/10.33322/kilat.v10i1.1174
Hendrastuty, N. (2024). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa. Jurnal Ilmiah Informatika Dan Ilmu Komputer (Jima-Ilkom), 3(1), 46–56. https://doi.org/10.58602/jima-ilkom.v3i1.26
Luchia, N. T., Handayani, H., & Hamdi, F. S. (2022). Perbandingan K-Means dan K-Medoids Pada Pengelompokan Data Miskin di Indonesia. Indonesian Journal of Machine Learning and Computer Science, 2(October), 35–41. https://doi.org/https://doi.org/10.57152/malcom.v2i2.422
R, N. N. F., Anggraeni, D. S., & Enri, U. (2022). Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient. Tematik, 9(1), 29–35. https://doi.org/10.38204/tematik.v9i1.901
Saputra, S. N., Haerani, E., Jasril, J., Oktavia, L., & Syafria, F. (2023). Penerapan Algoritma K - means Pada Cluster Penerima Bantuan Pangan Non Tunai (BPNT). Journal of Computer Engineering, System and Science, 8((2)), 438–449. https://doi.org/https://doi.org/10.24114/cess.v8i2.48026
Sari, Y. A. (2021). Pengaruh Upah Minimum Tingkat Pengangguran Terbuka Dan Jumlah Penduduk Terhadap Kemiskinan Di Provinsi Jawa Tengah. Equilibrium : Jurnal Ilmiah Ekonomi, Manajemen Dan Akuntansi, 10(2), 121–130. https://doi.org/10.35906/je001.v10i2.785
Siregar, H. A., Azlan, A., & Lumban Gaol, N. Y. (2023). Penerapan Data Mining Pada Penjualan Rumah Makan Kasih Ibu Menggunakan Metode K-Means Clustering. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 2(5), 750. https://doi.org/10.53513/jursi.v2i5.8955
Sitorus, Z., & Suhartika. (2024). Penerapan Data Mining Untuk Clustering Penduduk Miskin Di Kota Tanjungbalai Menggunakan Metode Algoritma K-Means. Journal of Science and Social Research, 4307(1), 212–218. https://doi.org/https://doi.org/10.54314/jssr.v7i1.1732
Supriyadi, A., Triayudi, A., & Sholihati, I. D. (2021). Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 6(2), 229–240. https://doi.org/10.29100/jipi.v6i2.2008
Syaputri, D., Noprita, P. H., & Romelah, S. (2021). Implementasi Algoritma K-Means untuk Pengelompokan Distribusi Sosial Ekonomi Masyarakat Berdasarkan Demografi Kependudukan. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 1(1), 1–6. https://doi.org/10.57152/malcom.v1i1.5
Taryadi, T. (2022). Klasterisasi Data Keluarga Pra Sejahtera Di Kota Pekalongan Dengan Metode K-Means Clustering. Jurnal Litbang Kota Pekalongan, 20(1), 70–76. https://doi.org/10.54911/litbang.v20i1.180
Triyana, M., Juita, R., & Suhendra, C. D. (2022). Penerapan Metode K-Means dalam Pengelompokan Data Penduduk Tidak Mampu di Distrik Oransbari. INFORMAL: Informatics Journal, 7(3), 220. https://doi.org/10.19184/isj.v7i3.34722
Utomo, W. (2021). The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia. ILKOM Jurnal Ilmiah, 13(1), 31–35. https://doi.org/10.33096/ilkom.v13i1.763.31-35
Waruwu, A., Yetri, M., & Setiawan, F. (2023). Implementasi Data Mining Dalam Mengelompokkan Data penduduk Kurang Mampu Menggunakan Metode K-Means Clustering. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 2(6), 945. https://doi.org/10.53513/jursi.v2i6.8965
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Pages: 679-687
Copyright (c) 2025 Tomi Ikhsan, Elin Haerani, Fitri Wulandari, Fadhilah Syafria
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