Digital Image Analysis of Beef Color Using Euclidean Distance Method
Hanny Hikmayanti Handayani, Deden Wahiddin · 2018 Third International Conference on Informatics and Computing (ICIC) · 2018
Beef color is an important indicator in determining the quality of beef. Unfortunately, currently there is no quantitative and standardized method for classifying and analyzing the color of beef. In the SNI 3932: 2008 Document any procedures undertaken for the assessment of the quality of beef are done organoleptically (Organoleptic test or sensory test is a way of testing by using the human senses as the primary means of measuring the reception power of the product) using the sense of sight of physical appearance of muscle and fat. The physical appearance value of meat and fat is then determined by using standard quality assistive devices. This method is highly subjective and unstable, as well as the speed of slow classification process with low accuracy. In this research, the image color classification system of beef is done by extracting RGB image feature of beef image by using Color feature extraction method to RGB (Red, Green, Blue). The method used to classify the color of the beef image is the Euclidean Distance method. Results obtained from the Color Classification System of Beef Image using Euclidean Distance Method in this study has an accuracy of 60%.