Analysis Of Poverty Level Mapping  In Riau Province Using The K-Means Method

Erlisa Santri; Dede  Brahma

doi:10.59934/jaiea.v3i2.414

Authors

Erlisa Santri Riau University
Dede Brahma Indonesian Islamic University

DOI:

https://doi.org/10.59934/jaiea.v3i2.414

Keywords:

Riau Province, Machine Learning, K-Means algorithm, Silhouette Score

Abstract

This research aims to understand the pattern of poverty distribution in Riau Province, identify clusters that reflect similar characteristics and provide a basis for developing more targeted policies. This approach uses machine learning techniques, especially the K-Means algorithm, to form clusters based on poverty level data. The results of the analysis show cluster 0 (C0) with a high poverty level and cluster 1 (C1) with a low poverty level. K-Means proved effective in grouping areas with similar levels of poverty, and provided a strong foundation for further analysis. Evaluation results using the Adjusted Rand Index (ARI), Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index show that the quality of cluster formation is good. This analysis provides detailed insight into poverty patterns in Riau Province and provides an empirical basis for implementing more contextual policies.

Downloads

Download data is not yet available.

References

Nugraha, I. W. (2023). Clustering Pemetaan Tingkat Kemiskinan di Provinsi Jawa Barat Menggunakan Algoritma K-Means. Jurnal Ilmiah Wahana Pendidikan, 234-244.

Nugroho Irawan Febianto, N. D. (2019). Analisis Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018. Jurnal SISFOKOM, 130-140.

Nabila Nur Fransiska R, D. S. (2018). Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient . Jurnal Teknologi Informasi Komunikasi (e-Journal), 29-35.

Wicaksono, A. E. (2016). Implementasi Data Mining Dalam Pengelompokan Data Peserta Didik Di Sekolah Untuk Memprediksi Calon Penerima Beasiswa Dengan Menggunakan Algoritma K-Means (Studi Kasus Sman 16 Bekasi). Jurnal Teknologi Rekayasa , 206-216.

Gustientiedina, M. A. (2019). Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan Pada RSUD Pekanbaru. Jurnal Nasional Teknologi Dan Sistem Informasi, 017-024.

Witten, e. a. (2012). Data Mining Practical Maachine Learning Tools and Technique. San Fransisco: 2nd Edition.

Tifani Amalina, D. B. (2022). Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food. Jurnal Ilmiah Wahana Pendidikan, 574-583

Mohammad Guntara, N. L. (2023). Cacah Klasterisasi dengan Algoritma K-Means Menggunakan Silhouette Coeficient. Jurnal Teknologi Informasi, 043-052.

Godwin Oghuabor, U. F. (2018). Clustering Algorithm For A Healthcare Dataset Using Silhouette Score Value. International Journal of Computer Science & Information Technology, 027-037

BPS Provinsi Riau URL: https://riau.bps.go.id/indicator/23/77/1/jumlah-penduduk-miskin.html