Deep Learning Based MobileNet Optimization For High Accuracy Classification Of Toddler Stunting - Wibowo, Anan; Sembiring, Rahmat Widia; Solikhun, Solikhun (Jurnal Teknik Informatika (Jutif), 2026)
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.