ANALISIS TEKSTUR PADA CITRA MOTIF BATIK UNTUK KLASIFIKASI MENGGUNAKAN K-NN - Kristian Adi Nugraha; Widi Hapsari; Nugroho Agus Haryono (Jurnal Informatika, 2015)
Indonesian’s Batik is one of culture heritage that recognized around the world. Batik has many variations of pattern based on their region. In this research, Batik would be used as subject for texture feature extraction. The value of this feature extraction would be used for classification using K-Nearest Neighbor (K-NN) method. Texture Feature Extraction components that used in this research were Entropy, Correlation, Homogeneity, and Energy. This research will investigate which component would give dominant effect for Batik’s pattern recognition. Batik pattern used in this research is pattern from Yogyakarta region. There are four patterns namely Ceplok, Parang, Semen, and Nitik. The result showed that there was no component from Texture Feature Extraction that gave dominant effect (average = 53,96%). Component with the highest value of accuracy is Correlation with a percentage of 55,83%. Whereas for K-NN classification, the best accuracy is 60% for K = 5.