Implementation of Convolutional Neural Network for Waste Classification Using the TrashNet Dataset
Keywords:
Convolutional Neural Network, Deep Learning, Image Classification, TrashNet, Artificial IntelligenceAbstract
Effective waste management requires a fast and accurate waste sorting process. This study aims to develop a Convolutional Neural Network (CNN)-based model for automatic waste classification using the TrashNet dataset. The dataset consists of six categories: cardboard, glass, metal, paper, plastic, and trash. The research methodology includes data preprocessing, data augmentation, CNN model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the proposed CNN model achieves a high level of classification accuracy on the test dataset, indicating its potential for supporting automated waste sorting systems.
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