Evaluasi Validitas Model Machine learning pada Klasifikasi Stunting Berbasis Data Antropometri dan Hubungan Deterministik
-
Turwan Aldi Putra
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Nirwana Hendrastuty Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
Stunting is a chronic nutritional problem among infants and toddlers that affects children’s growth and development. Various studies have utilized machine learning for nutritional status classification based on anthropometric data; however, the validity of the resulting models has rarely been examined. This study aims to evaluate the validity of machine learning models in classifying stunting status using the XGBoost, Random Forest, and Naïve Bayes algorithms. The dataset consists of 120,999 anthropometric records of infants, with age, gender, and height as features, and nutritional status as the target variable. The research process included preprocessing, data transformation, and model evaluation using the k-fold cross-validation method with accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest and XGBoost achieved very high accuracy, at 99.91% and 99.08%, respectively, while Naïve Bayes reached only 55%. This stark difference in performance indicates that ensemble-based models are capable of capturing very strong patterns in the data, while Naïve Bayes struggles due to the interdependence among features. Furthermore, the high accuracy of certain models suggests a deterministic relationship between features and labels, which could potentially make the models less robust against data containing measurement errors or noise.
Downloads
References
UNICEF, “Survei Status Gizi Indonesia (SSGI) 2024: Kemajuan Berkelanjutan Dalam Mengatasi Malnutrisi Anak,” Jakarta, Indonesia, 2025.
World Health Organization, “Levels And Trends In Child Malnutrition,” 2023. doi: 10.4060/cc2952en.
Mundirin, Idawati, and I. Latif, “Klasifikasi Status Gizi Balita Berbasis Data Antropometri Menggunakan Random Forest,” Journal of Computer Science and Informatics Engineering, vol. 04, no. 4, pp. 324–333, 2025, doi: 10.55537/cosie.v4i4.1202.
M. I. Elim and E. Utami, “Performance Comparison Of Child Stunting Prediction Support Vector Machine Vs Random Forest With Grid Search Optimization,” JUTIF : Jurnal Teknik Informatika, vol. 6, no. 5, pp. 5305–5319, 2025, doi: 10.52436/1.jutif.2025.6.5.5285.
S. Bela, D. Widyawati, and I. R. Yunita, “Comparative Analysis Of Random Forest , Logistic Regression And SVM For Stunting Prediction Using Anthropometric Data,” Journal of Information Systems and Informatics, vol. 7, no. 4, pp. 4271–4293, 2025, doi: 10.63158/journalisi.v7i4.1387.
E. Prasetyo and K. Nugroho, “Optimasi Klasifikasi Data Stunting Melalui Ensemble Learning Pada Label Multiclass Dengan Imbalance Data,” Jurnal Teknologi dan Informasi, vol. 23, no. 1, pp. 1–10, 2024, doi: 10.62411/tc.v23i1.9779.
B. Rao, M. Rashid, and G. Hasan, “Machine Learning In Predicting Child Malnutrition : A Meta-Analysis Of Demographic And Health Surveys Data,” International Journal of Environmental Research and Public Health, pp. 1–15, 2025, doi: 10.3390/ijerph22030449.
L. Wynants et al., “Prediction Models For Diagnosis And Prognosis Of Covid-19 : Systematic Review And Critical Appraisal,” Journal BMJ, pp. 1–22, 2020, doi: 10.1136/bmj.m1328.
S. Kapoor and A. Narayanan, “Article Leakage And The Reproducibility Crisis In Machine- Learning-Based Science Leakage And The Reproducibility Crisis In Machine-Learning-Based Science,” Patterns, vol. 4, no. 9, p. 100804, 2023, doi: 10.1016/j.patter.2023.100804.
F. Chen, L. Wang, J. Hong, J. Jiang, and L. Zhou, “Unmasking Bias In Artificial Intelligence : A Systematic Review Of Bias Detection And Mitigation Strategies In Electronic Health Record-Based Models,” Journal of the American Medical Informatics Association, vol. 31, no. March, pp. 1172–1182, 2024, doi: 10.1093/jamia/ocae060.
A. Subbaswamy, C. Science, M. Hall, and N. C. Street, “From Development To Deployment : Dataset Shift , Causality , And Shift-Stable Models In Health AI,” Journal Biostatistics, pp. 345–352, 2020, doi: 10.1093/biostatistics/kxz041.
S. E. Davis, M. E. Matheny, S. Balu, and M. P. Sendak, “Perspective A Framework For Understanding Label Leakage In Machine Learning For Health Care,” Journal ofthe American Medical Informatics Association, vol. 31, pp. 274–280, 2024, doi: 10.1093/jamia/ocad178.
G. S. Collins et al., “TRIPOD + AI Statement : Updated Guidance For Reporting Clinical Prediction Models That Use Regression Or Machine Learning Methods,” Journal BMJ, 2024, doi: 10.1136/bmj-2023-078378.
Kementrian Kesehatan Republik Indonesia, “Hasil Survei Status Gizi Indonesia (SSGI) 2022,” Jakarta, Indonesia, 2023.
C. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King, “Key Challenges For Delivering Clinical Impact With Artificial Intelligence,” BMC Pediatrics, pp. 1–9, 2019, doi: 10.1186/s12916-019-1426-2.
D. Azriani, D. Agustian, Y. Zuhairini, I. N. Yulita, and M. Dhamayanti, “Prediction Models For Stunting At 2-Years-Old From Indonesian Newborn Population,” BMC Pediatrics, 2025, doi: 10.1186/s12887-025-06096-4.
A. Hendy, R. K. Ibrahim, S. Mohammed, F. Abdelaliem, and A. Zaher, “Supervised Machine Learning For Classification And Prediction Of Stunting Among Under-Five Egyptian Children,” BMC Pediatrics, 2025, doi: 10.1186/s12887-025-06138-x.
A. Hannun, “Measuring Data Leakage In Machine-Learning Models With Fisher Information,” UAI : Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, no. Uai, pp. 760–770, 2021, [Online]. Available: https://proceedings.mlr.press/v161/hannun21a.html
A. M. A. Rahim, B. P. Hartato, A. Ridwan, and F. Asharudin, “Machine Learning-Based Approach For HIV / AIDS Prediction : Feature Selection And Data Balancing Strategy,” JAIC: Journal of Applied Informatics and Computing, vol. 9, no. 2, pp. 338–347, 2025, doi: 10.30871/jaic.v9i2.9125.
T. Hidayat, H. F. Kurniawan, and A. H. Nugroho, “Machine Learning Untuk Klasifikasi Gizi Balita Menggunakan Algoritma Random Forest,” InComTech: Jurnal Telekomunikasi dan Komputer, vol. 15, no. 2, pp. 125–136, 2025, doi: 10.22441/incomtech.
T. Yarkoni and J. Westfall, “Choosing Prediction Over Explanation In Psychology: Lessons From Machine Learning,” Journal HHS Public Access, vol. 12, no. 6, pp. 1100–1122, 2019, doi: 10.1177/1745691617693393.
World Bank Group, “Levels And Trends In Child Malnutrition,” 2023. [Online]. Available: https://data.unicef.org/resources/jme-report-2023/
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Evaluasi Validitas Model Machine learning pada Klasifikasi Stunting Berbasis Data Antropometri dan Hubungan Deterministik
Pages: 62-72
Copyright (c) 2026 Turwan Aldi Putra, Nirwana Hendrastuty
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
