📅 27 July 2024
DOI: 10.26877/asset.v6i3.673
Implementation of DenseNet121 Architecture for Waste Type Classification
Advance Sustainable Science, Engineering and Technology
Universitas Persatuan Guru Republik Indonesia Semarang
📄 Abstract
The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency
🔖 Keywords
#Waste; Classification; DenseNet; Deep Learning
ℹ️ Informasi Publikasi
Tanggal Publikasi
27 July 2024
Volume / Nomor / Tahun
Volume 6, Nomor 3, Tahun 2024
📝 HOW TO CITE
Zulhusni, Munis; Sari, Christy Atika; Rachmawanto, Eko Hari, "Implementation of DenseNet121 Architecture for Waste Type Classification," Advance Sustainable Science, Engineering and Technology, vol. 6, no. 3, Jul. 2024.
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