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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Information System KOMPUTIKA - Jurnal Sistem Komputer Indonesian Community Service and Empowerment Journal (IComSE) Al-MIKRAJ: Jurnal studi Islam dan Humaniora Jurnal Pengabdian Pelitabangsa Indonesian Journal of Infomatics
Sri Nurhayati
Sistem Komputer, Fakultas Teknik Dan Ilmu Komputer, Universitas Komputer Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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The Effect of Data Imbalance on the Interpretation Stability of LIME-Based Explainable AI on Nutritional Status Prediction Models Sri Nurhayati; Hidayat Hidayat; Siti Ar-Rachmi Ningrum; Zainal Arifin Hasibuan; Sri Supatmi
Indonesian Journal of Infomatics Vol. 1 No. 2 (2026): May: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i2.438

Abstract

Data imbalance is a common challenge in nutritional status prediction because it can reduce classification performance and influence the reliability of Explainable Artificial Intelligence (XAI) interpretations. This study aims to examine the impact of data imbalance on the stability of Local Interpretable Model-Agnostic Explanations (LIME)-based interpretations. A Random Forest model was developed under two scenarios: using the original imbalanced dataset and using a balanced dataset generated through the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated and compared, followed by LIME-based interpretation and stability analysis. The results indicate that SMOTE enhanced the model’s ability to identify minority classes, with recall increasing from 0.36 to 0.55, although overall accuracy slightly declined. LIME analysis revealed changes in feature contributions between the two scenarios, reflecting the influence of data distribution on model explanations. The interpretation stability score reached 0.80, suggesting relatively consistent explanations despite variations in class balance. These findings highlight the importance of jointly evaluating predictive performance and interpretation stability in health-related machine learning applications.
Co-Authors Agus Nursikuwagus Andrias Darmayadi Diana Effendi Hani Irmayanti Hidayat Hidayat Hidayat, Hidayat Iman Immanudin Mochamad Fajar Wicaksono Myrna Dwi Rahmatya R H Nugraha Riani Lubis Siti Ar-Rachmi Ningrum Sri Supatmi Syahrul Syahrul Usep Mohamad Ishaq Wicaksono, Mochamad Fajar Zainal Arifin Hasibuan