Klasifikasi Motif Kain Tenun menggunakan K-Nearest Neighbor Berdasarkan Gray Level Co-occurrence Matrix - James Umbu Kaya Ngg Behar (Jurnal Informatika dan Teknologi Komputer (J-ICOM), 2022)
Woven cloth is one type of archipelago craft art, namely textile craft that has existed until now, but recognizing the type of fabric or motif is not an easy thing so a classification process is needed. Gray level co-occurrence matrix is an extraction method used which is then used as color and shape characteristics to obtain contrast, energy and homogeneity values. Classification was carried out based on the results of the previous extraction using the k-nearest neighbors classification method. The purpose of classification is to divide the image of weaving into motif classes according to the pattern of the motif so that it is easy to identify according to its characteristics. The fabric image identification process uses Matlab R2017b software. K-nearest neighbors in classifying 2 sets of fabric data with a total of 20 datasets for training data and 2 sets of test data each with 10 data shows that the k-nearest neighbors method is seen from the level of testing the value of k=1 k= 3 and k=5 the accuracy obtained is 100%. the k-nearest neighbors method is good in classifying the types of woven fabric motifs.