Identification Of Coffee Fruit Maturity Level Using Machine Learning Based Color Classification With Comparison Of K-nearest Neighbor (K-NN) And Method Support Vector Machine (SVM)

Abstract

Coffee is one of Indonesia's main export commodities which has high economic value. The maturity of coffee berries is an important factor in determining the quality and price of coffee, therefore, developing a method for identifying the level of maturity of coffee berries using image processing is an effective solution. The aim that the author wants to achieve is to obtain a comparison of the performance of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) to obtain better accuracy values in determining the ripeness of coffee cherries. It was found that for the accuracy value of the image of ripe, quite ripe and raw coffee fruit, namely, the accuracy value obtained using KNN was 98.40%, providing better accuracy compared to SVM which had an accuracy value of 86.90%.