Analysis of Family Status at Risk of Stunting in Babat District using the K-Means Clustering Method

Susmita Amelia

doi:10.59934/jaiea.v6i1.2827

Authors

Susmita Amelia UNIVERSITAS ISLAM LAMONGAN

DOI:

https://doi.org/10.59934/jaiea.v6i1.2827

Keywords:

stunting, Analysis, Clustering, K-Means Algorithm, data mining

Abstract

Stunting is a national public health priority because it affects children's physical growth, cognitive development, and the quality of future human resources. To support stunting prevention efforts, it is important to identify areas with similar risk characteristics so that intervention programs can be implemented more effectively. This study aims to analyze the clustering of villages in Babat Regency based on stunting risk using the K-Means clustering algorithm. The variables used include the number of infants under two years of age, the number of children under five years of age, the number of pregnant women, and environmental factors represented by access to proper sanitation and clean water sources. A total of 21 villages were analyzed and grouped into three clusters representing high, medium, and low stunting risk. The clustering results showed that five villages were classified as high risk, four villages as medium risk, and 12 villages as low risk. These results were then compared with the original village data and showed that the clustering accurately represented the actual conditions, indicating that the K-Means algorithm effectively identified villages with similar stunting risk characteristics. These findings indicate that K-Means clustering can provide valuable information for local governments in determining intervention priorities, allocating resources efficiently, and improving the effectiveness of stunting prevention and management programs in Babat Regency.

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References

[1] D. Yusuf, F. Razi, S. A. Arman, V. Terisia, and R. Nurjayanti, “Prediksi Risiko Stunting pada Balita menggunakan Algoritma Logistic Regression dan Decision Tree berbasis Data Terbuka,” Pros. Semnastek, pp. 1–7, 2025.

A. S. Hibatulloh and U. Zaky, “APLIKASI DETEKSI DINI STUNTING PADA BATITA MENGGUNAKAN KLASIFIKASI TINGGI BADAN TERHADAP UMUR BERDASARKAN Z-SCORE WORLD HEALTH ORGANIZATION,” (Jurnal Mhs. Tek. Inform., vol. 9, no. 6, pp. 9441–9448, 2025.

N. Nur, M. Iqram, and N. Inayah, “Perbandingan K-Means dan Hierarchical Clustering dalam Pengelompokan Daerah Beresiko Stunting,” pp. 356–367, 2023.

A. Subayu, “PENERAPAN METODE K-MEANS UNTUK ANALISIS STUNTING GIZI PADA BALITA :,” vol. 2, no. 1, 2022, doi: 10.20885/snati.v2i1.18.

M. F. Amalia and D. B. Arianto, “IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING DALAM KLASTERISASI KABUPATEN / KOTA PROVINSI JAWA BARAT BERDASARKAN FAKTOR PEMICU STUNTING PADA BALITA,” vol. 9, no. 1, pp. 36–46, 2024.

I. S. Tinendung and I. Zufria, “Pengelompokan Status Stunting Pada Anak Menggunakan Metode K-Means Clustering,” vol. 7, pp. 2014–2023, 2023, doi: 10.30865/mib.v7i4.6908.

N. Nafiiyah, R. Wardhani, M. N. Huda, and A. P. Setyan, “K -means clustering for data-driven traffic accident in east java,” vol. 10, no. 4, pp. 3679–3688, 2025.

D. M. Cytry, S. Defit, and G. W. Nurcahyo, “Jurnal KomtekInfo Penerapan Metode K-Means dalam Klasterisasi Status Desa terhadap Keluarga Beresiko Stunting,” vol. 10, no. 3, pp. 122–127, 2023, doi: 10.35134/komtekinfo.v10i3.423.

P. Apriyani, A. R. Dikananda, and I. Ali, “Penerapan Algoritma K-Means dalam Klasterisasi Kasus Stunting Balita Desa Tegalwangi,” 2023.

N. S. Hanapiah et al., “MENINGKATKAN PENANGANAN STUNTING PADA ANAK MELALUI KLASIFIKASI PEMBERIAN MAKANAN TAMBAHAN BERDASARKAN USIA DENGAN METODE K-MEANS DI DESA CINTARASA,” vol. 7, no. 6, pp. 3484–3493, 2023.

M. R. Nugroho, R. N. Sasongko, and M. Kristiawan, “Faktor-faktor yang Mempengaruhi Kejadian Stunting pada Anak Usia Dini di Indonesia,” J. Obs. J. Pendidik. Anak Usia Dini, vol. 5, no. 2, pp. 2269–2276, 2021, doi: 10.31004/obsesi.v5i2.1169.

S. Nurul Rizki Octaviyani1, Rini Mayasari2 and Received:, “Implementasi Algoritma K-Means Clustering Status Gizi Balita Nurul Rizki Octaviyani 1 , Rini Mayasari 2 , Susilawati 3 Universitas Singaperbangsa Karawang,” vol. 8, no. 13, pp. 370–381, 2022.

4Baenil Huda 1Fuad Anwar Yuwono, 2 April Lia Hananto, 3Fitri Nurapriani, “Implementasi Algoritma K-means untuk Klasterisasai Data Stunting di Kabupaten Bekasi,” vol. 9, no. 2, pp. 626–633, 2025.

T. Hardiani and R. N. Putri, “Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Stunting Pada Balita,” Digit. Transform. Technol., vol. 4, no. 1, pp. 621–627, 2024, doi: 10.47709/digitech.v4i1.4481.

A. Fikri, B. F. Hutabarat, and U. Khaira, “Komparasi K-Means Clustering Dan Complete Linkage Dalam Pengelompokan Penyaluran Pinjaman Financial Technology,” vol. 17, no. 2, pp. 228–239, 2023.