A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification

Effective waste management represents a global challenge with significant environmental and public health impacts. Despite existing waste classification systems achieving high accuracy rates, a critical research gap exists in determining optimal CNN architectures for real-world deployment constraint...

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Principais autores:	Raffa Adhi Kumala, Christy Atika Sari, Eko Hari Rachmawanto
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Politeknik Negeri Batam 2025-08-01
coleção:	Journal of Applied Informatics and Computing
Assuntos:	classification convolutional neural network mobilenetv2 vgg16 waste management
Acesso em linha:	https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9958
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