Pengenalan dan Edukasi Motif Batik Untuk Sekolah Dasar Negeri Pondok Bahar 06 Menggunakan Metode Convolution Neural Network (CNN)

Abstract

Batik is a cultural heritage of Indonesia rich in philosophical values and diverse motifs. However, a deep understanding of its meaning remains limited among elementary school students. This study aims to develop an educational application based on Convolutional Neural Networks (CNN) to introduce and classify batik motifs such as Kawung, Parang, Megamendung, and Truntum in an interactive manner. The batik image dataset was obtained from various online sources and underwent preprocessing, augmentation, training, and testing stages using the CNN model. The developed application was then tested with students from SD Negeri Pondok Bahar 06 using a pre-test and post-test method. Test results indicated that the CNN model was able to recognize batik motifs with adequate accuracy. Moreover, there was a significant improvement in students’ understanding of the philosophical meanings behind the motifs after using the application. Thus, integrating CNN technology into cultural learning proves to be effective in enhancing student interest and comprehension. This research is expected to serve as a reference for developing AI-based educational media to preserve local culture in the digital era.