CLUSTERING DAERAH RAWAN STUNTING  DI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS

Cahya Syalom; Ulfa Khaira; Daniel Arsa

doi:10.46576/djtechno.v6i1.6223

CLUSTERING DAERAH RAWAN STUNTING DI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS

Cahya Syalom, Ulfa Khaira, Daniel Arsa

Abstract

Stunting is one of the main nutritional problems in Indonesia whose prevalence is still high, which is 21.5% in 2023, exceeding the WHO standard which should be below 20%. This study aims to identify areas at risk of stunting in Indonesia using the K-Means method based on factors that cause stunting. The data used includes coverage of essential services and stunting cases from 38 provinces during 2022-2024, obtained from the Ditjen Bina Bangda Kemendagri dashboard as much as 3534 data. The research begins by determining the variables that have a stronger correlation with stunting among other variables in each year using Spearman correlation. Then, the optimal number of clusters was determined using the Silhouette Coefficient method. The results show that the optimal number of clusters for each year is K=2 with each value being 0.387 (2022), 0.436 (2023), and 0.446 (2024). Cluster 1 represents high stunting risk areas with low coverage of essential services, while cluster 2 represents low stunting risk areas with more optimized essential services. In 2022, cluster 1 consists of 25 provinces, and cluster 2 consists of 13 provinces. In 2023, cluster 1 decreases to 18 provinces and cluster 2 increases to 20 provinces. In 2024, cluster 1 declines further to 14 provinces, while cluster 2 increases to 24 provinces.

Keywords

Stunting; K-Means; Clustering; Silhouette Coefficient

Full Text:

PDF

References

WHO, “Stunting in a nutshell,” World Health Organization. Accessed: Oct. 07, 2024. [Online]. Available: https://www.who.int/news/item/19-11-2015-stunting-in-a-nutshell

M. Ariani, “Determinan Penyebab Kejadian Stunting Pada Balita: Tinjauan Literatur,” Dinamika Kesehatan Jurnal Kebidanan dan Keperawatan, vol. 11, no. 1, pp. 2549–4058, 2020, doi: 10.33859/dksm.v11i1.

Kementerian Kesehatan RI, “Laporan SKI TEMATIK 2023,” Kementerian Kesehatan RI. Accessed: Dec. 20, 2024. [Online]. Available: https://www.badankebijakan.kemkes.go.id/laporan-tematik-ski/

Kementerian Kesehatan RI, “Prevalensi Stunting di Indonesia Turun ke 21,6% dari 24,4%,” Biro Komunikasi dan Pelayanan Publik. Accessed: Oct. 11, 2024. [Online]. Available: https://kemkes.go.id/id/%20prevalensi-stunting-di-indonesia-turun-ke-216-dari-244

R. Rotul Muhima et al., Kupas Tuntas Algoritma Clustering: Konsep Perhitungan Manual Dan Program. 2021.

P. Aselnino and A. W. Wijayanto, “Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment,” Indonesian Journal of Applied Statistics, vol. 6, no. 1, p. 57, Jan. 2024, doi: 10.13057/ijas.v6i1.68876.

M. Handayani and M. F. L. Sibuea, “Performance Analysis of Clustering Models Based on Machine Learning in Stunting Data Mapping,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 9, no. 4, pp. 715–720, Sep. 2023, doi: 10.33330/jurteksi.v9i4.2770.

N. Nur Afidah and M. Masrukan, “Penerapan Metode Clustering dengan Algoritma K-means untuk Pengelompokkan Data Migrasi Penduduk Tiap Kecamatan di Kabupaten Rembang,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 6, pp. 729–738, 2023, Accessed: Dec. 10, 2024. [Online]. Available: https://journal.unnes.ac.id/sju/prisma/article/view/67038

T. A. Cinderatama et al., “Implementasi Metode K-Means, Dbscan, dan Meanshift Untuk Analisis Jenis Ancaman Jaringan Pada Intrusion Detection System,” Jurnal INOVTEK Polbeng, vol. 7, no. 1, pp. 169–184, 2022, doi: 10.35314/isi.v7i1.2336.

D. Fakta Sari, A. Kusjani, D. Kurniawati, and I. Setiawan, “PENCARIAN DATA QUICK COUNT PILPRES DENGAN TEKNIK WEB SCRAPING,” JIRK (Journal of Innovation Research and Knowledge), vol. 3, no. 5, pp. 1025–1034, 2023, Accessed: Jan. 14, 2025. [Online]. Available: https://bajangjournal.com/index.php/JIRK/article/view/6695

F. Marisa, A. L. Maukar, and T. Muhammad Akhriza, Data Mining Konsep dan Penerapannya. 2021.

A. Iswahyudi Yasril, F. Fatma, and D. Febrianti, “Penerapan Uji Korelasi Spearman Untuk Mengkaji Faktor yang Berhubungan Dengan Kejadian Diabetes Melitus di Puskesmas Sicincin Kabupaten Padang Pariaman,” Jurnal Human Care, vol. 6, no. 3, pp. 527–533, 2021, doi: 10.32883/hcj.v6i3.1444.

K. Seljuna Monika, N. Anggraini, and W. Rajagukguk, “Hubungan Insentif dan Lingkungan Kerja Terhadap Komitmen Organisasi Pada Karyawan Pt. Kb Finansia Multi Finance,” Fundamental Management Journal, vol. 7, no. 1, pp. 123–174, 2022, doi: 10.33541/fjm.v7i1p.3888.

M. Riszky Sulaeman, T. Nur Padilah, U. Singaperbangsa Karawang, J. HSRonggo Waluyo, T. Timur, and J. Barat, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Kecelakaan Berkendara Di Ruas Tol Jakarta-Cikampek,” Jurnal informasi dan Komputer, no. 1, p. 11, 2023, doi: 10.35959/jik.v11i01.369.

D. A. I. C. Dewi and D. A. K. Pramita, “Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” Matrix: Jurnal Manajemen Teknologi dan Informatika, vol. 9, no. 3, pp. 102–109, 2019, doi: 10.31940/matrix.v9i3.1662.

DOI: https://doi.org/10.46576/djtechno.v6i1.6223