Arif Budianto
Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram NTB 83125

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Waste Classification Model Optimization with Modified MobileNetV3 for Efficient Waste Management Putri Andani; Ramadian Ridho Illahi; I Wayan Sudiarta; Marzuki; Arif Budianto
Jurnal Fisika dan Aplikasinya Vol 21 No 2 (2025): June 2025 Edition
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v21i2.20876

Abstract

The increase in population and economic activity has a significant impact on the amount of waste. Data in 2023 states that waste in Indonesia still cannot be managed properly. One solution to overcome this problem is through recycling with waste sorting as a crucial stage. This research develops a waste classification model using modified MobileNetV3S. The classification process is performed using Convolutional Neural Net- work (CNN) method and parameter fine-tuning. This model is able to classify five different categories of waste, namely plastic bottles, leaves, plastic sheets, paper, and metal. These categories were chosen by considering the common practice in waste classification for recycling purposes. The results show that the validation accuracyreaches 96.2% with a loss value of 0.049. These results can significantly contribute to better and sustainable waste management efforts.