Stunting Risk Cluster Analysis In Petatal Plantation Village Using K-Means Clustering Approach
DOI:
https://doi.org/10.24235/itej.v11i1.299Keywords:
Stunting, K-Means Clustering; Cluster Analysis; Nutritional Risk; Perkebunan Petatal Village.Abstract
Stunting is a chronic nutritional issue that requires accurate, data-driven intervention. This study aims to map the level of stunting risk among toddlers in Perkebunan Petatal Village by categorizing them into high, medium, and low-risk groups. The research method employed is descriptive quantitative, utilizing the K-Means Clustering algorithm to group toddler data based on related risk indicators. The analysis results revealed three risk clusters: Cluster 1 (high risk) consisting of 14 toddler data, Cluster 2 (medium risk) consisting of 17 toddler data, and Cluster 3 (low risk) consisting of 19 toddler data. These findings indicate that while the majority of toddlers fall into the low-risk category, there are still toddlers in the high and medium-risk categories who require specific attention from village authorities and health workers. This information serves as a crucial basis for determining nutritional intervention priorities and designing more targeted and data-driven stunting prevention programs in Perkebunan Petatal Village
Downloads
References
[1] Sulistiani et al., “Data Analysis of Student Monitoring Using the K-Means Clustering Method,” Indones. J. Mod. Sci. Technol., vol. 1, no. 2, pp. 50–57, 2025, doi: 10.64021/ijmst.1.2.50-57.2025.
[2] E. A. Wibowo and R. Aryanti, “Penerapan Metode Clustering K-Means Menggunakan RapidMiner untuk Klasifikasi Prestasi Siswa di Sekolah Swasta,” J. Inf. Technol. Informatics Eng., vol. 1, no. 1, pp. 20–24, 2025, [Online]. Available: https://journal.jci.co.id/jitie/article/view/35
[3] I. Fazira and Z. F. Risawandi, “Klasterisasi Tingkat Kelayakan Provinsi Dalam Pembangunan Kawasan Industri Menggunakan Algoritma K-Means,” vol. 10, no. 2, pp. 874–886, 2025.
[4] J. N. Iin, “Perbandingan Kmeans Dan Hierarchical Clustering Untuk Pemetaan Kawasan Rawan Stunting Di Kabupaten Kolaka,” vol. 10, no. 2, pp. 701–716, 2025.
[5] M. Fadhil and W. Fuadi, “Clustering Tingkat Kecanduan Game Mobile Legends Terhadap Keharmonisan Keluarga Menggunakan Metode K-Means,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 10, no. 2, pp. 981–990, 2025.
[6] R. RAHMAWATI and W. Prihartono, “Optimasi stok dengan clustering data transaksi penjualan menggunakan algoritma K-Means di Konter Agung Cell,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 2, 2025.
[7] H. Azari, D. Hartanti, and A. A. Sari, “Pengelompokan Produksi Padi dan Beras Provinsi Jawa Timur dengan Metode Agglomerative Hierarchical Clustering,” Infotek J. Inform. dan Teknol., vol. 7, no. 2, pp. 379–389, 2024.
[8] J. Homepage et al., “Implementasi Algoritma K-Means untuk Pengelompokan Data Penjualan Berdasarkan Pola Penjualan,” vol. 5, no. 1, pp. 350–358, 2025.
[9] A. Alawiyah, N. Aghnia, and F. F. Abdalah, “Implementasi Clustering Algoritma K-Means Pada Penjualan Beras Di CV Tangguh Bumi Perkasa,” J. Komisi (Jurnal Komput. dan Sist. Informasi), vol. 2, no. 2, pp. 17–23, 2025.
[10] M. Adelina Bui and A. Bahtiar, “Implementasi Metode Algoritma K-Means Clustering Untuk Mengelompokkan Transaksi Penjualan Barang Di Toko Arino,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 1451–1456, 2024, doi: 10.36040/jati.v8i2.8975.
[11] N. Bili, R. T. Abineno, and A. A. Pekuwali, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Peforma Siswa Pada Pembelajaran Bahasa Indonesia,” in Prosiding Seminar Nasional SATI, 2024, pp. 523–537.
[12] W. Usino, “Klasterisasi Tingkat Kelayakan Provinsi Dalam Pembangunan Kawasan Industri Menggunakan Algoritma K-Means,” vol. 3, no. September, pp. 324–333, 2024.
[13] N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. dan Ilmu Komput., vol. 3, no. 1, pp. 46–56, 2024, doi: 10.58602/jima-ilkom.v3i1.26.
[14] D. D. Susilo, “Implementasi Data Mining Pada Pengelompokkan Data Pembeli Menggunakan Algoritma K-Means Pada PT. Otomotif,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, 2024.
[15] R. Farismana, “Penerapan K-Means Clustering Untuk Pemetaan Produktivitas Padi Dan Prediksi Panen Di Kabupaten Indramayu,” J. Inf. Syst. Applied, Manag. Account. Res., vol. 8, no. 3, p. 589, 2024, doi: 10.52362/jisamar.v8i3.1572.
