Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm

Abstract

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