• Iwan Jaya Bakti Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia
  • Nirwana Hendrastuty Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia
Keywords: Batik Motif, Canny Operator, Classification, Convolutional Neural Network, DenseNet121


Batik, as Indonesia's cultural heritage, has high artistic value and has a variety of unique motifs.. The main focus of this research is to solve the problem of the complexity and diversity of motifs found in Indonesian batik culture. The Canny operator is used as a first step to extract the edges of batik motifs, with the aim of improving the quality of feature extraction before entering the classification stage using CNN, specifically by using the DenseNet121 model. The dataset of this study was obtained through the Kaggle platform, published by Dionisius Darryl Hermansyah. The platform consists of 983 images (.jpg) with 20 different Indonesian batik motifs. Pre-processing includes the use of Canny for edge detection and data augmentation to increase the diversity of the dataset. Next, variations in the number of epochs and batch size were used to train the model. The results show that in the first test, the use of the Canny operation gives a higher confidence level in the model. In the model with Canny, there is a 1.6% increase in accuracy (33.57% with Canny and 31.97% without Canny). In addition, there are differences in the level of confidence in some batik classes. For example, the "batik mega mendung" class shows an increase in confidence of 66.57% with Canny (88.53% with Canny and 21.96% without Canny), while the "batik sekar" class shows a decrease in confidence of 12.09% with Canny.


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