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All Journal Journal of Information Systems and Informatics
Sarwindah
Institut Sains dan Bisnis Atma Luhur
Computer Science & IT

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Ensemble Learning for Pediatric Stunting Detection: A Comparative Study of XGBoost, Random Forest, and LightGBM with Oversampling Techniques Tri Sugihartono; Djoko Soetarno; Rahmat Sulaiman; Sarwindah; Marini; Fitriyani
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1568

Abstract

Stunting, driven by chronic childhood malnutrition, remains a critical global public health concern. Early detection is persistently challenged by class imbalance in pediatric health datasets and the absence of systematic comparisons between oversampling strategies and ensemble classifiers. This study develops and evaluates an ensemble learning pipeline for stunting detection, benchmarking XGBoost, Random Forest, and LightGBM across five oversampling configurations — Original, SMOTE, ADASYN, Borderline-SMOTE, and SMOTE-ENN — using 10,000 pediatric health records from posyandu activities in Bangka Belitung Province, Indonesia. Seven anthropometric and demographic features were utilized, with stratified 80:20 train-test splitting and five-fold cross-validation. XGBoost with original imbalanced data achieved the highest Recall (0.9573) and a competitive F1-Score (0.9158), while LightGBM with SMOTE delivered the strongest balanced performance (F1-Score: 0.9160, ROC-AUC: 0.8431). SMOTE-ENN consistently underperformed across all classifiers. To our knowledge, this is the first study to simultaneously compare five oversampling strategies across three ensemble models within a unified framework, offering a foundation for high-sensitivity stunting surveillance in resource-constrained healthcare settings.
Co-Authors Djoko Soetarno Fitriyani Marini Rahmat Sulaiman Tri Sugihartono