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Journal Zetroem
Published by Universitas PGRI Banyuwangi
ISSN : 2656081X     EISSN : 2656081X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
jurnal zetroem yang dapat dimuat dalam jurnal ini meliputi bidang keilmuan Teknik Elektronika, Teknik Kendali, Sistem Tenaga, Telekomunikasi, Informatika, Sistem Distribusi. Makalah dapat berupa ringkasan laporan hasil penelitian atau kajian pustaka ilmiah. Makalah yang akan dimuat hendaknya memenuhi format yang telah ditentukan.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 171 Documents
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Predicting Independent Z-Score Stunting Through Fundamental Anthropometric Measurements Utilizing Extreme Gradient Boosting (XGBoost) Muhammad Resha; Apriana Toding
ZETROEM Vol 8 No 2 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i2.8736

Abstract

Early detection of stunting generally relies on the WHO standard Z-Score calculation, which requires specific instruments, thus limiting diagnostic efficiency in resource-constrained areas. This study proposes a data-centric Machine Learning approach to precisely predict stunting status using only basic anthropometric data (age, sex, weight, and height) without involving Z-Score derived variables. The study evaluated a large-scale post-pandemic dataset consisting of 40,071 medical records of toddlers from Jeneponto Regency, Indonesia.Given the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Comprehensive benchmarking experiments were conducted using XGBoost, Random Forest, LightGBM, and Logistic Regression algorithms. The empirical evaluation results demonstrated that the XGBoost algorithm produced the best classification performance with an accuracy of 97.88%, outperforming Random Forest (95.77%) and registering a significant margin of advantage of 15.36% compared to the baseline linear model.Feature Importance analysis confirmed that Height (52.41%) and Age (28.15%) were the most fundamental predictors. These findings indicate that tree-ensemble-based architectures are capable of capturing complex non-linear correlations between basic anthropometric parameters. The implications of this study offer a scientific foundation for the development of lightweight, low-cost, and automated stunting diagnosis systems independent of conventional Z-Score calculations at integrated health posts in developing regions

Page 18 of 18 | Total Record : 171

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