Implementation of CNN Algorithm for Classification of Organic and Inorganic Waste Images

The increasing waste problem necessitates efficient solutions, one of which is automatic classification based on artificial intelligence. This study develops a Convolutional Neural Network (CNN) model for classifying organic and inorganic waste images using a transfer learning approach with the MobileNetV2 architecture. The model was trained in two stages, namely feature extraction and fine-tuning, using a dataset of 25,077 images from a public Kaggle repository. The results show that the model after fine-tuning achieved an accuracy of 92.28%, with a precision of 89.6% for the organic category and 96.4% for inorganic. High recall and F1-score values were also achieved, demonstrating that transfer learning with fine-tuning effectively improves waste image classification accuracy and has potential for implementation in automatic waste sorting systems.