waste classification; transfer learning; EfficientNet-B0

waste classification; transfer learning; EfficientNet-B0

Recycling of wasteis a significant challenge in modern waste management. Conventional techniques that useinductive and capacitive proximity sensors exhibit limitations in accuracy and flexibility for the detection ofvarious types of waste. Indonesia generates approximately 175,000 tons of waste per day, highlighting the urgent need for efficient waste management solutions.Thisstudy develops a waste classification system based on deep learning, leveraging the powerful EfficientNet-B0 model through transfer learning. EfficientNet-B0 is designed with a compound scaling method, which uniformly scales network depth, width, and resolution, providing an optimal balance between accuracy and computational efficiency. The model was trained on a dataset containing six classes of waste—glass, cardboard, paper, metal, plastic, and residue—totalling7014 images. The model was trained using data augmentation and fine-tuning techniques. The training results show a test accuracy of 91.94%, a precision of 92.10%, and a recall of 91.94%, resulting in an F1-score of 91.96%. Visualisationof predictions demonstrates that the model effectively classifies waste in new test data. Implementing this model in the industry can automate the waste sorting process more efficiently and accurately thanmethods based on inductive and capacitive proximity sensors. This study underscores the significant potential of deep learning models, particularly EfficientNet-B0, in industrial waste classification applications and opens opportunities for further integration with sensor and robotic systems for more advanced waste management solutions

waste classification; transfer learning; EfficientNet-B0

Risfendra1, Gheri Febri Ananda2*, Herlin Setyawan

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5875/961

Departmentof Electrical Engineering, Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia

25-08-2024

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