Deep Learning Based MobileNet Optimization For High Accuracy Classification Of Toddler Stunting

Anan  Wibowo; Rahmat Widia  Sembiring; Solikhun Solikhun

doi:10.52436/1.jutif.2026.7.2.5382

Authors

Anan Wibowo Informatics, STIKOM Tunas Bangsa Pematangsiantar, Indonesia
Rahmat Widia Sembiring Informatics, Politeknik Negeri Medan, Indonesia
Solikhun Informatics, STIKOM Tunas Bangsa Pematangsiantar, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5382

Keywords:

Deep Learning, Image Classification, MobileNet, Stunting

Abstract

This study aims to develop and optimize a MobileNet-based deep learning model for toddler stunting classification using whole-body images. A progressive optimization strategy was applied through three scenarios: (1) a baseline MobileNet feature-extraction model, (2) an optimized fine-tuned model, and (3) a final model enhanced with an adaptive ReduceLROnPlateau scheduler. Using a private dataset of 571 images, the proposed model achieved significant improvements—from 97.47% accuracy in the baseline model to a perfect 100% accuracy, precision, recall, and F1-score in the final scenario. These results highlight the novelty of this study, namely the use of whole-body images combined with progressive MobileNet optimization, which substantially outperforms prior studies relying solely on facial image analysis. The proposed approach demonstrates strong potential as a highly accurate and efficient computational tool for clinical stunting screening.

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