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September 10, 2025

Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model

Key Points

Accuracy of 99.58% in waste classification indicates a significant improvement in sorting performance.
The Morph-HSV color model enhanced CNN's ability to categorize organic and inorganic waste types effectively.
A dataset from Kaggle was processed using Python to train the classification model accurately.
Efficient waste management can be achieved through advanced image processing and machine learning techniques.

Abstract

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.

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