Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Haerul Yuda Aditiya
Universitas Buana Perjuangan Karawang
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Transfer learning-based malnutrition classification using VGG16 and comparative analysis of CNN architectures Ahmad Fauzi; Haerul Yuda Aditiya; Maharina Maharina; Sihabudin Sihabidin; Muhammad Ansari Adista; Iflan Naufal; Natasya Eka Nanda Sonia Puri; Candra Zonyfar
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11346

Abstract

Childhood malnutrition remains a critical public health challenge in developing countries, with Indonesia ranking fifth globally for stunting burden. Traditional anthropometric assessment methods are time-consuming, resource-intensive, and require trained personnel, necessitating efficient computer-based early detection approaches. This study proposes a deep learning-based method for automated nutritional status classification using facial image analysis. We developed and compared multiple transfer learning architectures including visual geometry group 16 (VGG16), densely connected convolutional network 121 (DenseNet121), mobile network version 2 (MobileNetV2), and residual network 50 (ResNet50) for classifying children into three categories: healthy, malnutrition, and stunting. Results demonstrated that VGG16, a simpler architecture trained for only 10 epochs, achieved optimal performance with 91.8% accuracy, and significantly outperforming more complex modern architectures like ResNet50. This finding challenges the conventional assumption that newer, deeper models invariably perform better, and particularly when working with limited medical datasets. The study revealed that longer training durations led to performance degradation due to overfitting, emphasizing the importance of balancing model complexity with dataset characteristics. These findings support the development of practical artificial intelligence (AI)-based malnutrition screening systems suitable for resource-constrained environments, potentially improving early detection capabilities, and public health outcomes in developing regions.
Co-Authors Ahmad Fauzi Candra Zonyfar Iflan Naufal Maharina Maharina Muhammad Ansari Adista Natasya Eka Nanda Sonia Puri Sihabudin Sihabidin