Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM)

Abstract

Digital image processing is part of the technological developments in the concepts and reasoning, the human wants the machine (computer) can recognize images like human vision. Recognizing the image is one way to distinguish the traits that exist in the image. Texture is one of the characteristics that distinguish the image, is the basic characteristic of the image identification. Gray Level Co-Occurrence Matrix (GLCM) is one method of obtaining characteristic texture image by calculating the probability of adjacency relationship between two pixels at a certain distance and direction. The characteristics of texture obtained from GLCM methods include contrast, correlation, homogeneity, and energy. The extracted features are then used for identification with the nearest distance calculations (Eucledian Distance). The final results analysis program to identify the category of apples raw, half-ripe or overripe. Training data used are 12 images apple, consisting of 4 is crude, 4 is half-cooked, and 4 is ripe, 7 data used for testing. Testing GLCM with 00 angle feature extraction results of the test images can be recognized by a factor Eucledian Distance to the query image. Identification of test data is information all the data can be recognized. Eucledian Distance is a method that helps the introduction of a test object data.