Klasifikasi Kualitas Buah SawitMenggunakan Metode Gray Level Co-occurrence Matrix Dengan Variasi Arah Obyek

Abstract

The harvest of fresh fruit bunches (FFB) from palm fruit is done with fruit that is of good quality, namely fruit that is dark red in color or has lots of knots, so that the quality of the palm oil produced increases. Low quality produced will of course further weaken competitiveness. Determining the quality classification of palm oil can be done through image processing techniques. Classification is the process of declaring a data object to one of the previously defined categories. Categorization of data sets can involve grouping them into similar types of classification or identifying similar characteristics across a number of observations. Classification is supported by the concept of pattern recognition, classifying the quality of palm oil products as categories produced. Efforts to achieve the expected quality standards by using quality control. This classification of palm oil is divided into quality classes, namely good and not good. Knowing the quality of palm oil is done by making an application for palm oil quality using image processing. There are quite a lot of methods used in image processing technology. One of them is the Gray Level Co-Occurrence Matrix (GLCM) method. Gray Level Co-Occurrence Matrix (GLCM) is a method used for texture analysis/feature extraction, feature acquisition is obtained from matrix pixel values, which have certain values ​​and form an angle pattern. Palm Fruit Quality Classification Using Gray Level Co-occurrence Matrix With Variations in Object Direction. It is hoped that this application can help determine the quality of palm fruit more easily and efficiently.