Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model - Fahmi, Miftahuddin; Yudhana, Anton; Sunardi; Abdel-Nasser Sharkawy; Furizal (Scientific Journal of Engineering Research, 2025)
Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.