Implementation of Convolutional Neural Network in Image-Based Waste Classification

The increasingly complex issue of waste management, particularly in the sorting process, demands efficient and accurate technology-based solution. This study aims to implement the Convolutional Neural Network (CNN) method for image-based waste classification, focusing on two classes paper and plastic. The dataset used consists of 2000 images, with an 80% proportion for training and 20% for testing. This study tested four scenarios combining image augmentation and classification methods, namely threshold and one-hot encoding, and evaluated model performance using accuracy, precision, recall, and F1-score metrics. The best results were obtained in the scenario using image augmentation with the one-hot encoding classification method, with an accuracy of 89%, precision of 88.5%, recall of 89%, and F1-score of 88.5%. These findings indicate that implementation of CNN can enhance the effectiveness of image-based waste classification and support recycling efforts through a smarter and more automated sorting system.