Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN - Husnul Khotimah; Nur Nafi’iyah; Masruroh (LPPM Politeknik Jambi, 2019)
identification or classification using image processing and computer vision requires pattern recognition from the training dataset. The process of image processing and pattern recognition becomes a highly developed research study. Starting from the process of recognizing an object, or classification of objects and about detecting the level of fruit maturity. This research will classify the level of maturity of mangoes with HSV images. Where the RGB input image is converted to HSV. Then the average values of HSV intensity, skewness, and kurtosis are taken. The process of classification comes into 4 classes: raw, fairly ripe, ripe and very ripe. With the KNN classification method, and the dataset used 129 training data, and 40 testing data. The highest accuracy value at k = 2 is 80%. The tool used to develop the system is matlab.