期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：24
页码：6915
出版社：Journal of Theoretical and Applied
摘要：Combed cotton and viscose cotton (CVC) is 2 types of cotton commonly used as the main ingredient in manufacture of clothes. Clothes made from cotton combed known as good materials for clothes. CVC cotton, made by combining cotton combed and cotton viscose so that in the manufacture CVC cotton quality is assumed under the combed cotton. Manually distinguish combed cotton and CVC cotton is by wearing clothes made of combed cotton and CVC cotton. Basically the texture in combed cotton and CVC fabrics can be analyzed by image texture segmentation process based on image texture analysis to get patterns showing the type of cotton. In this research explains the texture of cotton that through the process of image texture segmentation can release the value of features that can be used for the classification process, so that can determine the type of cotton. In this image texture segmentation includes grayscaling process, image normalization and texture feature extraction. Feature Extraction uses the Gray Level Run Length (GLRLM) method to get a feature value on the texture used for the classification process. Classification using method euclidean distance with 4 testing images and 2 training images by changing the original image to 5x5 and 10x10 pixels generate 100% accuracy. These results indicate that accuracy using the euclidean distance method results in high accuracy.
