SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

Waste is a significant problem that is around us. The problem occurs because waste volume speed could be faster. This problem can be solved by implementing machine learning in the waste sorting process based on two categories which are organic and inorganic. Knowing the most efficient image processing and classification machine learning model is necessary. This research uses the Support Vector Machine classification model hybridized with the Convolutional Neural Network, image processing morphology, and the HSV color model. The dataset is collected from the images available on the Kaggle website and executed using Python. The data used amounted to 25,077 with a training and test data ratio of 85:15. The data is processed using the proposed method, namely the morphology and HSV color model, to determine the performance between using the image process and those that do not. The data that has been processed is classified using the SVM-CNN Hybrid classification model. The performance results are an accuracy rate of 99.34% and a loss of 1.67% without overfitting.