Identifikasi Tingkat Kematangan Buah Pinang Menggunakan K-Nearest Neighbor Berdasarkan Fitur Tekstur dan Warna

This research builds a system for identifying the maturity level of areca fruit based on digital image processing using texture and color features through Gray Level Co-Occurrence Matrix (GLCM) Color moments. The initial stage is pre-processing so that it can be processed to next stage, namely feature extraction. Texture extraction was performed (GLCM), correlation value moments, mean used in this study. Classification done have been extracted before. study uses K-Nearest Neighbor (KNN) classification method. Tests were carried out determine parameters cause changes results with scenarios including determining number Neighbors KNN. By 1 KNN classifier, best accuracy 86.36% process fruit.