K-Means Clustering with Elbow Method for Stunting Risk Detection in Toddlers Using Anthropometric and Nutritional Data

Irma Darmayanti; Dhanar Intan Surya Saputra; Anugerah Bagus Wijaya; Andik Wijanarko; Dewi Fortuna; Aldrian Firmansyah Putranto

doi:10.63158/journalisi.v7i4.1337

Irma Darmayanti Amikom Purwokerto University, Indonesia
Dhanar Intan Surya Saputra Amikom Purwokerto University, Indonesia
Anugerah Bagus Wijaya Amikom Purwokerto University, Indonesia
Andik Wijanarko Amikom Purwokerto University, Indonesia
Dewi Fortuna Telkom University, Indonesia
Aldrian Firmansyah Putranto Amikom Purwokerto University, Indonesia

DOI: 10.63158/journalisi.v7i4.1337

Keywords: Stunting, K-Means Clustering, Toddler, Nutrition, Anthropometric Data

Abstract

Stunting remains a critical public health challenge in Indonesia, primarily due to inadequate nutrition and recurrent infections in early childhood. This study aimed to identify patterns of stunting risk by integrating anthropometric and dietary data, specifically sugar consumption, using an unsupervised machine learning approach. A total of 20 toddlers aged 12-59 months from Purwokerto Selatan participated. Anthropometric data (age, weight, height) and dietary intake (sugar consumption, snack frequency) were collected via a caregiver questionnaire. K-Means clustering was applied, with the optimal number of clusters determined using the Elbow Method (K=2). Two clusters were identified: Cluster 0, with a lower risk of stunting, and Cluster 1, with a higher proportion of toddlers at risk. Cross-tabulation with stunting status validated this, showing that Cluster 1 contained more children with "Potential" stunting. Internal validation using the Silhouette score (0.252) and PCA visualization confirmed the clustering's robustness. This study demonstrates the potential of combining anthropometric and dietary data for stunting risk profiling, suggesting a complementary approach for growth monitoring programs and targeted interventions.

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References

N. I. Irsanti and I. Rodiyah, “Peran Posyandu Edelways Dalam Upaya Penanganan Stunting Di Desa Grogol Kecamatan Tulangan Kabupaten Sidoarjo,” J. Publicuho, vol. 8, no. 3, pp. 1943–1962, 2025.

Mivtahurrahimah, S. K. M., Epid, M., & Sevtiyani, I., "Unhealthy lifestyle and limited health monitoring as key risk factors for diabetes mellitus: Evidence from the 2023 Indonesian health survey," J. Epidemiol. Kesehat. Komunitas, vol. 10, no. 3, 2025.

Nugroho, M. R., Sasongko, R. N., & Kristiawan, M., "Faktor-faktor yang mempengaruhi kejadian stunting pada anak usia dini di Indonesia," J. Obsesi: J. Pendidik. Anak Usia Dini, vol. 5, no. 2, pp. 2269-2276, 2021.

K. BKPK, Buku Saku Hasil Studi Status Gizi Indonesia (SSGI) Tahun 2021. Kementrian Kesehatan Republik Indonesia, 2021.

A. Daracantika, Ainin, and Besral, “Systematic Literature Review: Pengaruh Negatif Stunting terhadap Perkembangan Kognitif Anak,” J. BIKFOKES, vol. 1, no. 2, 2021, doi: 10.7454/bikfokes.v1i2.1012.

Siregar, P. A. S., "Urgensi penerapan kebijakan cukai atas minuman berpemanis dalam kemasan (MBDK) di Indonesia," ETHNOGRAPHY: J. Design, Soc. Sci. Hum. Stud., vol. 2, no. 1, pp. 01-11, 2025.

D. Sulistiawati, “Agensi anak dalam pembentukan kebiasaan jajan balita dengan status gizi kurang di Rawa Bogo, Bekasi,” Antropol. Indones., vol. 44, no. 1, pp. 1–15, 2023, doi: 10.7454/jai.v44i1.1021.

I. Darmayanti, D. Intan, S. Saputra, and I. Saputri, “Clustering Sugar Content in Children ’ s Snacks for Diabetes Prevention Using Unsupervised Learning,” vol. 6, no. 4, pp. 2923–2936, 2024, doi: 10.51519/journalisi.v6i4.932.

S. L. Aila, F. F. Dieny, A. Candra, and H. S. Wijayanti, “Added Sugars Consumption Decreased Iron and Zinc Intake among Children Aged 24-59 Months in Central Java Konsumsi Gula Tambahan Menurunkan Asupan Zat Besi dan Seng pada Anak,” Amerta Nutr., vol. 7, no. 2, pp. 47–57, 2023, doi: 10.20473/amnt.v7i2SP.2023.47.

W. Jin, “Research on Machine Learning and Its Algorithms and Development,” J. Phys. Conf. Ser., 2020, doi: 10.1088/1742-6596/1544/1/012003.

Nurhayati, Busman, and R. P. Iswara, “Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis Di Media Sosial Sebagai Media Promosi,” J. Tek. Inform., vol. 12, no. 1, pp. 79–96, 2019.

N. Chapwanya and K. N. Gorejena, “Hybrid Unsupervised Machine Learning for Insurance Fraud Detection : PCA-XGBoost-LOF and Isolation Forest,” J. Inf. Syst. Informatics, vol. 7, no. 1, pp. 941–959, 2025, doi: 10.51519/journalisi.v7i1.958.

R. S. Nurhalizah and R. Ardianto, “Analisis Supervised dan Unsupervised Learning pada Machine Learning : Systematic Literature Review,” J. Ilmu Komput. dan Inform., vol. 4, no. 1, pp. 61–72, 2024.

J. Yu et al., “Dietary Sugar Research in Preschoolers: Methodological, Genetic, and Cardiometabolic Considerations.,” Rev. Cardiovasc. Med., vol. 24, no. 9, p. 259, Sep. 2023, doi: 10.31083/j.rcm2409259.

Apriyani, P., Dikananda, A. R., & Ali, I., "Penerapan algoritma K-Means dalam klasterisasi kasus stunting balita desa Tegalwangi," Hello World J. Ilmu Komput., vol. 2, no. 1, pp. 20-33, 2023.

D. Cytry, S. Defit, and G. Nurcahyo, “Penerapan Metode K-Means dalam Klasterisasi Status Desa terhadap Keluarga Beresiko Stunting,” J. KomtekInfo, pp. 122–127, Sep. 2023, doi: 10.35134/komtekinfo.v10i3.423.

I. P. Sari, Al-Khowarizmi, O. K. Sulaiman, and D. Apdilah, “Implementation of Data Classification Using K-Means Algorithm in Clustering Stunting Cases,” J. Comput. Sci. Inf. Technol. Telecommun. Eng., vol. 4, no. 2, pp. 402–412, 2023, doi: 10.30596/jcositte.v4i2.15765.

M. A. Aziz, L. Amalia, and I. Darmayanti, “Comparison of K-Medoids Algorithm with K- Means on Number of Student Dropped Out,” 1st Int. Conf. Smart Technol. Appl. Informatics, Eng., pp. 53–58, 2022.

C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm,” EURASIP J. Wirel. Commun. Netw., 2021, doi: 10.1186/s13638-021-01910-w.

I. Darmayanti, D. Mustofa, N. Hidayati, and I. Saputri, “K - Means and Fuzzy C - Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment,” vol. 13, pp. 2175–2182, 2024.

M. Shutaywi and N. N. Kachouie, “Silhouette analysis for performance evaluation in machine learning with applications to clustering,” Entropy, vol. 23, no. 6, pp. 1–17, 2021, doi: 10.3390/e23060759.

K. R. Shahapure and C. Nicholas, “Cluster Quality Analysis Using Silhouette Score,” in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, pp. 747–748. doi: 10.1109/DSAA49011.2020.00096.

D. Wulandari, T. Prahasto, and V. Gunawan, “Penerapan Principal Component Analysis untuk Mereduksi Dimensi Data Penerapan Teknologi Informasi dan Komunikasi untuk Pendidikan di Sekolah,” vol. 02, pp. 91–96, 2016, doi: 10.21456/vol6iss2pp91-96.