Classification of tomato maturity levels based on RGB and HSV colors using KNN algorithm

Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. According to (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One way that can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processing techniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue and Hue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simple with good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highest accuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classified the maturity of tomatoes by utilizing the color image of RGB and HSV.