Comparison of Machine Learning Algorithm Results Based on HSV Color Model for Image Classification of Vegetable Types

Abstract

Currently, research on the classification of vegetables has made many advances. Machine learning has been proposed in recent years and has been created in image recognition, computer vision, and other fields. This study aims to classify vegetable products as part of the research of the classification of objects in charge that are inherently more complex than other subsets of object classification. This study will use the K-Nearest Neighbor (KNN) model to classify vegetable species, but with the addition of HSV color space model features. To see the performance of K-Nearest Neighbor (KNN) against other machine learning algorithms, a comparison will be made with support vector machine algorithms, logistic regression and naïve bayes. From the experimental results, the KNN algorithm got an accuracy of 80.67%, SVM got an accuracy of 72.23%, LR got an accuracy of 61.19%, NB got an accuracy of 48.77% and HSV-KNN got an accuracy of 84.33%.