SISTEM CERDAS MENDETEKSI KUALITAS GULA MERAH MENGGUNAKAN METODE NEAREST MEAN CLASSIFIER (NMC)

Abstract

Listen

Translate article

The development and utilization of digital images has developed rapidly. At present, digital image processing capabilities and techniques make it possible to be used more effectively and efficiently in identifying quality classes of brown sugar. One of them is the concept of Smart Systems with the use of Matlap-based applications so that public recognition of the importance of selecting good quality brown sugar can be a little more efficient. Digital image processing capabilities are supported by the concept of pattern recognition and classification, it is expected that the quality classification of brown sugar based on RGB color variables (Red, Green, Blue) and texture variables (energy, contrast, correlation and homogeneity) with the help of computers can be realized. To get a solution of the problem of classification and determine the accuracy of the classification of the quality of brown sugar into a certain class, then we need a method that is able to classify the quality of brown sugar brown sugar into class A (very good), class B (good) and class C (not good ). The method is expected to also be able to handle the problem of the accuracy of the classification of brown sugar into certain quality classes according to the actual state of brown sugar.