<![CDATA[Manually, humans can easily detect and differentiate types of food. But man sees certainly has contrains that meant identification a kind of food inconsistantly different. Rresearches show that human vision influenced by age, disease and physical condition. Through the passing technology, researchers have seen the emergence of food's recognition systems in image processing. The aim of this study is to develop a system that could recognize kinds of food based on it's color and texture by using K-NN. The process was proceded by image segmentation process. The segmented image then used to gain the color feature extraction's value, color channels that use RGB as the channel with 9 sub features and the texture value of feature extraction, GLCM with 20 sub features at angle 0 , 45 , 90 , and 135 . The results is this extracted feature then used in the process of image classification using K-NN. Testing process done through 3 stages that are k value testing, feature extraction testing, distance calculation method testing with 900 data sets of two types of data categories. The result is data which use value k =3 that earn as much as 90,58% of accuration with balanced composition data.]]>
Copyrights © 2019
