Classification Of Batik Keraton and Pesisir Imagery Using Convolutional Neural Network


  • Novi Ambar Wati ,Wulan Pertiwi ,Florensia ,Dani Daffa Pratama A ,Satria Rohman Atthariq ,Sukenda


Convolutional Neural Network, Multi Layer Perceptron, Batik Keraton , Pesisir Imagery


Convolutional Neural Network (CNN) is a machine learning method of Multi-Layer Perceptron (MLP) development designed for 2-dimensional data processing. Therefore, we have conducted a literature study on CNN that is used to classify the image of keraton and coastal batik. The results of this literature study can be seen from the accuracy of the classification of batik imagery using CNN. IThis this paper explains the methods, features, and also the application of CNN for the classification of batik imagery, especially keraton batik and coastal batik. We used the sequential method for CNN and implemented it using Google Collab. In this study you classified 4 batik motifs namely Batik Kawung, Batik Lasem, Batik Megamendung, and Batik Parang. The accuracy rate obtained in this study was 92%.