Pengenalan dan Edukasi Motif Batik Untuk Sekolah Dasar Negeri Pondok Bahar 06 Menggunakan Metode Convolution Neural Network (CNN)
Article Sidebar
Main Article Content
Abstract
Article Summary
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.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
Dasar, D. S. (2020). Asesmen Diagnostik. Direktorat Sekolah Dasar, Direktorat Jenderal PAUD Dikdas Dan Dikmen, Kementrian Pendidikan Dan Kebudayaan, Riset Dan Teknologi.
DLY, I. A. (2023). Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data. Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data, 4(4), 1176-1185.
Fanani, G. P. I., Mu'min, M. A., Safitri, Y., Wijaya, S. A., Tristanti, N., & Famuji, T. S. (2025). Pengenalan Citra Batik Tradisional Menggunakan Deep Learning untuk Klasifikasi Motif Daerah. Scientific: Journal of Computer Science and Informatics, 2(1), 1-7. https://doi.org/10.34304/scientific.v2i1.336.
Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. (No Title).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Mitayani, N. M., & Astuti, I. D. A. S. W. (2024). Pengembangan Batik Khas Kebudayaan Indonesia Dengan Pengaruh Teknologi. Prosiding Pekan Ilmiah Pelajar (PILAR), 4, 60-64.
Nurmawan, R., Khaq, M., & Anjarini, T. (2021). Pengembangan Multimedia Batik Khas Purworejo untuk Meningkatkan Aspek Psikomotorik Halus pada Mata Pelajaran Seni Budaya dan Prakarya. Jurnal Kualita Pendidikan, 2(2), 159-165. https://doi.org/10.51651/jkp.v2i2.61.
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep learning dan penerapannya dalam pembelajaran. JIIP-Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258-3267. https://doi.org/10.54371/jiip.v5i9.805.
Tusriyanto, T. (2020). Pengembangan model pembelajaran terpadu berbasis budaya lokal di SD kota Metro. Elementary: Jurnal Iilmiah Pendidikan Dasar, 6(1), 59-72.
Utari, L., & Zulfikar, A. (2023). Penerapan Convolutional Neural Networks Menggunakan Edge Detection Untuk Identifikasi Motif Jenis Batik. TeknoIS: Jurnal Ilmiah Teknologi Informasi dan Sains, 13(1), 110-123. https://doi.org/10.36350/jbs.v13i1.184.
Wibisono, M. R., Nugroho, I. M. R., & Defriani, M. (2025). Batik Motif Classification Using CNN With Resnet-50 Architecture. RISTEC: Research in Information Systems and Technology, 6(1), 41-59.
Wona, M. M. A., Asyifa, S. A., Virgianti, R., Hamid, M. N., Handoko, I. M., Septiani, N. W. P., & Lestari, M. (2023). Klasifikasi Batik Indonesia Menggunakan Convolutional Neural Network (CNN). Jurnal Rekayasa Teknologi Informasi (JURTI), 7(2), 172-179.
Yulisetiani, S. (2022). Merancang Bahan Ajar Digital Berwawasan Budaya Nusantara Untuk Pembelajaran Bahasa Indonesia Sekolah Dasar (Vol. 1). Jejak Pustaka.