Pembuatan Profil Status Gizi Balita Berbasis Pembelajaran Mesin Menggunakan Indikator Antropometri

Dianda Rifaldi; Galih Pramuja Inngam Fanani; Alya Masitha; Setiawan Ardi Wijaya; Irwandi Rizki Saputra

doi:10.30787/restia.v4i2.2658

Authors

Dianda Rifaldi Universitas Riau Indonesia
Galih Pramuja Inngam Fanani Universitas ‘Aisyiyah Surakarta
Alya Masitha Universitas ‘Aisyiyah Surakarta
Setiawan Ardi Wijaya Universitas Muhammadiyah Riau
Irwandi Rizki Saputra Universitas Riau Indonesia

DOI:

https://doi.org/10.30787/restia.v4i2.2658

Keywords:

Anthropometry, Toddlers, Machine Learning, Nutritional Status Profile

Abstract

Stunting and nutritional issues among children under five remain major public health challenges in Indonesia, requiring precise, data-driven monitoring. This study aims to establish nutritional status profiles for children under five using a machine learning approach based on anthropometric indicators. The methodology began with a data quality audit and Exploratory Data Analysis (EDA) on a dataset comprising 120,999 observations with no missing values. The dataset consisting of age, height, and gender attributes was split using a stratified approach (80:20) and subsequently standardized using StandardScaler and OneHotEncoder. Four classification algorithms (Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest) were evaluated comparatively. Experimental results demonstrated that Random Forest achieved the best performance, with an accuracy of 98.94%, a macro-averaged Precision of 98.74%, a macro-averaged Recall of 98.45%, and a macro-averaged F1-Score of 98.59%. The model's reliability and consistency were confirmed via 5-Fold Stratified Cross-Validation, yielding an average accuracy of 99.04% with a very low standard deviation (0.0009). Furthermore, feature importance analysis revealed that height (60%) and age (39%) were the most dominant anthropometric indicators in determining the nutritional status profiles of children under five. This study demonstrates that the Random Forest algorithm, based on anthropometric indicators, is suitable for implementation as a decision support system for growth monitoring and the early detection of nutritional issues in children.

References

R. Desmita, R. S. Siregar, V. Ivanda, Y. Hanoselina, and R. F. Helmi, "Peran tenaga kesehatan dalam pemberdayaan keluarga untuk pencegahan stunting," Cult. Educ. Technol., vol. 2, no. 1, pp. 177–190, 2025, doi: 10.31004/ctr.v2i1.123.

R. Imeldawati, "Dampak terjadinya stunting terhadap perkembangan kognitif anak: Literature review," J. Med. Nusant., vol. 3, no. 1, pp. 101–107, 2025, doi: 10.59680/medika.v3i1.1632.

A. Mulyana, Hendra, and Retnoningsih, "Implikasi stunting terhadap kemampuan kognitif dan fisik anak usia dini di TK At-Taqwa Teta Lambitu," J. Ilm. Pendidik. Dasar, vol. 10, no. 2, pp. 122–133, 2025.

S. Monica, T. C. Maigoda, and A. Krisnasary, "Literature review: Intervensi stunting di negara berkembang," Nutr. Heal. Insights, vol. 1, no. 2, pp. 87–97, 2024. [Online]. Available: https://journal.dapupublishing.com/NaHI/article/view/20

D. Sartika, M. Munawarah, and M. I. S, "Pengaruh konsumsi makanan bergizi pada balita terhadap stunting," J. Nurs. Pract. Educ., vol. 5, no. 1, pp. 1–9, 2024, doi: 10.34305/jnpe.v5i1.1370.

F. A. Nursifa, I. M. Hafiz, S. Khoirunnisa, H. Ridwan, and P. Haryeti, "Pemenuhan gizi pada seribu hari pertama kehidupan terhadap kejadian stunting di Indonesia: Literature review," J. Kesehat. Indra Husada, vol. 13, no. 1, pp. 82–89, 2025.

A. Istiqomah, K. M. S, R. A. Amali, and S. Tiawati, "Peran gizi terhadap pertumbuhan dan perkembangan balita," Antigen J. Kesehat. Masy. dan Ilmu Gizi, vol. 2, no. 2, pp. 67–74, 2024, doi: 10.57213/antigen.v2i2.260.

T. T. M. Ginting and A. Zebua, "Sosialisasi pencegahan stunting di Kelurahan Sei Mati Kecamatan Medan Maimun: Upaya peningkatan kesehatan ibu dan anak," J. Pengabdi. Masy. Bhinneka, vol. 3, no. 1, pp. 27–31, 2024, doi: 10.58266/jpmb.v3i1.97.

Y. P. D. T. Aulia and S. Tanuwidjaja, "Gambaran status gizi pada balita di Puskesmas Jatiwangi tahun 2024," Med. Sci., p. 8, 2024, doi: 10.29313/bcsms.v6i1.22623.

R. Falensya, "Pentingnya gizi seimbang dalam mencegah stunting pada anak usia dini," J. Innov. Educ., vol. 3, no. 4, pp. 8–21, 2025, doi: 10.59841/inoved.v3i4.3549.

C. Fauziyah, R. Hasibuan, A. A. Sholikah, M. S. Jenar, and A. Zahrotunnisa, "Dampak kurangnya asupan gizi pada perkembangan anak di masa pandemi," JIIP - J. Ilm. Ilmu Pendidik., vol. 8, no. 7, pp. 7684–7691, 2025, doi: 10.54371/jiip.v8i7.8742.

D. N. S. Permana, N. Uly, and A. Alim, "Efektivitas berbagai intervensi gizi dalam penanggulangan stunting pada anak: Tinjauan literatur," Borneo Nurs. J., vol. 7, no. 2, pp. 401–411, 2025, doi: 10.61878/bnj.v7i2.102.

A. Emilda, Alchalidi, and M. Sukmadewi, "Faktor-faktor yang mempengaruhi status gizi balita berdasarkan BB/U," J. Kesehat. Ibu dan Anak, vol. 6, no. 2, pp. 13–20, 2014.

S. Mau, F. B. D. P. Dopo, F. Tedy, and P. F. M. Tengangatu, "Implementasi algoritma Naive Bayes pada aplikasi web untuk klasifikasi status gizi balita," J. Inform. Polinema, vol. 12, no. 2, pp. 333–340, 2026, doi: 10.33795/jip.v12i2.8898.

J. Ipmawati and I. Unggara, "Analisis status gizi anak menggunakan metode klastering pada dataset anthropometri," bit-Tech, vol. 7, no. 2, pp. 494–504, 2024, doi: 10.32877/bt.v7i2.1869.

P. F. Ruziq and M. M. R. Wayahdi, "Implementasi algoritma K-Nearest Neighbor (K-NN) untuk klasifikasi data kesehatan," 2026. [Online]. Available: www.media.hadlacorp.com

D. Sartika, F. Elfaladonna, and A. Octarina, "Optimizing data preprocessing on stunting datasets: Identifying relevant attributes for machine learning analysis," in Proc. Int. Conf., 2025, pp. 481–501, doi: 10.2991/978-94-6463-678-9_45.

A. Masitha, S. Lonang, and J. M. Reski, "Machine learning approach for heart failure patient classification using K-Nearest Neighbors algorithm," Methods Sci. Technol. Stud., vol. 1, no. 2, pp. 81–88, 2025, doi: 10.64539/msts.v1i2.2025.44.

D. Rifaldi, A. Fadlil, and Herman, "Teknik preprocessing pada text mining menggunakan data tweet 'Mental Health'," Decode J. Pendidik. Teknol. Inf., vol. 3, no. 2, pp. 161–171, 2023, doi: 10.51454/decode.v3i2.131.

M. Hudaya, W. Nor, M. Yuliastina, and M. Nordiansyah, "Exploring stunting in South Kalimantan Province using R programming-based data visualization," Kesmas J. Kesehat. Masy. Nas., vol. 20, no. 3, pp. 213–221, 2025, doi: 10.7454/kesmas.v20i3.2267.

A. Heryati, T. Terttiaaivini, D. Marcelina, and H. Romli, "Optimization of stunting risk prediction using a hybrid genetic-machine learning model," J. Artif. Intell. Softw. Eng., vol. 5, no. 2, pp. 807–815, 2025. [Online]. Available: https://e-jurnal.pnl.ac.id/JAISE/article/view/6988

R. Ratnasari, A. J. Wahidin, and T. H. Andika, "Deteksi dini stunting pada anak berdasarkan indikator antropometri dengan menggunakan algoritma machine learning," J. Algoritm., vol. 21, no. 2, pp. 378–387, 2024, doi: 10.33364/algoritma/v.21-2.2122.

N. I. E. Harahap, L. H. Hasibuan, and S. Musthofa, "Model klasifikasi tinggi badan balita stunting menggunakan regresi logistik biner," JOSTECH J. Sci. Technol., vol. 5, no. 2, pp. 257–270, 2025, doi: 10.15548/jostech.v5i2.12420.

F. Millano, S. Kurniawan, and R. Gustriansyah, "Penerapan metode decision tree dalam klasifikasi status gizi balita," Komputa J. Ilm. Komput. dan Inform., vol. 14, no. 2, pp. 53–62, 2025, doi: 10.34010/komputa.v14i2.16468.

Maulina, "Analisis jejak stunting dengan prediksi kejadian pada anak balita menggunakan metode random forest," Skripsi, Univ. Islam Negeri, 2024.

R. Y. Pratama and A. Baita, "Prediksi stunting pada anak balita menggunakan algoritma extreme gradient boosting dan bayesian optimization," J. Technol. Informatics, vol. 7, no. 2, pp. 175–189, 2025, doi: 10.37802/joti.v7i2.1174.