PENERAPAN K-MEANS CLUSTERING UNTUK PENGELOMPOKAN WILAYAH BERDASARKAN TINGKAT KEMISKINAN DI INDONESIA

Authors

  • Sagita Maesarah Universitas Ibrahimy Author
  • Zaehol Fatah Universitas Ibrahimy Author

DOI:

https://doi.org/10.69714/3d5mkb02

Keywords:

Proverty, K-means clustering and grouping

Abstract

Poverty is one of the problems that hinder national and regional growth. Poverty is the inability to meet the minimum standards of basic needs including food and non-food needs. Poor people are people who are below a limit or called the poverty line. The resources used are sourced from www.kaggle.com. In the process of data processing with the k-means Clustering method. The K-means Clustering method is a method of grouping existing data into several groups where the data in one group has the same characteristics as each other and has different characteristics from the data in the group. The results of the study show that regions in Indonesia can be grouped into several clusters with different poverty level characteristics. These clusters reveal specific patterns, such as the concentration of areas with high poverty in certain areas and the factors that contribute to these conditions. With this approach, the government and policy makers can identify priority areas and design more effective programs to reduce poverty levels..

References

Arkham, D., & Swanjaya, D. (2020). K-Means Method For Clustering Public Service Assessment of Goverment Organization In Kediri City. Prosiding SEMNAS INOTEK, 155–160. https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/79

Bahauddin, A., Fatmawati, A., & Permata Sari, F. (2021). Analisis Clustering Provinsi Di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Algoritma K-Means. Jurnal Manajemen Informatika Dan Sistem Informasi, 4(1), 1–8. https://doi.org/10.36595/misi.v4i1.216

Dkk, A. K. (2015). Indikator Kemiskinan dan Misklasifikasi Orang Miskin (Ali Khomsa). november 2015.

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

Mayasari, S. N., & Nugraha, J. (2023). Implementasi K-Means Cluster Analysis untuk Mengelompokkan Kabupaten/Kota Berdasarkan Data Kemiskinan di Provinsi Jawa Tengah Tahun 2022. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 3(2), 317–329. https://doi.org/10.24002/konstelasi.v3i2.7200

Nabila, H., Retno, D., & Saputro, S. (2022). Clustering Data Campuran Numerik dan Kategorik Menggunakan Algoritme Ensemble Quick RObust Clustering using linKs ( QROCK ). Prisma, Prosiding Seminar Nasional Matematika, 5(1), 716–720. https://journal.unnes.ac.id/sju/index.php/prisma/article/view/54590

prof. Dr. Ir. Keppi Sukesi, M. (ed). (2015). GENDER & KEMISKINAN DI INDONESIA (Keppi Suke). juli 2015.

Sepriyanti, N., Sani Nahampun, R., Zikri, M. H., Ambarani, I., & Rahmadeyan, A. (2022). SENTIMAS: Seminar Nasional Penelitian dan Pengabdian Masyarakat Implementation of K-Means Clustering to Group Poverty Levels in Riau Province Penerapan K-Means Clustering Untuk Mengelompokkan Tingkat Kemiskinan di Provinsi Riau. Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 59–65. https://journal.irpi.or.id/index.php/sentimas

Uddin, M. B., & Fatah, Z. (2024). Gudang Jurnal Multidisiplin Ilmu Penerapan Data Mining Clustering K-Means Dalam Mengelompokkan Data Penduduk Penyandang Disabilitas. 2(November), 86–94.

Downloads

Published

2024-12-16

How to Cite

PENERAPAN K-MEANS CLUSTERING UNTUK PENGELOMPOKAN WILAYAH BERDASARKAN TINGKAT KEMISKINAN DI INDONESIA (Sagita Maesarah & Zaehol Fatah , Trans.). (2024). Jurnal Ilmiah Multidisiplin Ilmu, 1(6), 114-120. https://doi.org/10.69714/3d5mkb02