Klasifikasi Motif Batik Solo Menggunakan Convolutional Neural Network dengan Transfer Learning VGG16
-
Daffa Ferdinan Aditama
*
Universitas Teknologi Yogyakarta, Yogyakarta,
Indonesia
- RR. Hajar Puji Sejati Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia
- Fadil Indra Sanjaya Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
Batik Solo has a rich variety of motifs with high philosophical value, but the process of identifying motifs is still largely done manually, making it subjective and prone to error. This study aims to develop an automatic classification system to distinguish four Batik Solo motifs, namely Parang, Kawung, Truntum, and Sekar Jagad, using the Convolutional Neural Network (CNN) method based on the VGG16 architecture with a transfer learning approach. The dataset used consists of 280 batik images divided evenly into four classes (70 images per class), where data limitations are overcome using ImageNet pre-trained weights, freezing all convolution layers, and applying data augmentation to reduce the risk of overfitting. The selection of VGG16 was based on the consideration that this study focused on evaluating feature extraction capabilities and analyzing the classification performance of Batik Solo visual patterns in depth, so VGG16 was used as a stable and interpretative baseline model, not for the purposes of computational efficiency or mobile implementation. The training process was carried out for 50 epochs with a data division of 60% training data, 20% validation data, and 20% test data, and the test results showed an accuracy of 85.71% with average precision, recall, and F1-score values of 0.88; 0.86; and 0.86, respectively, where the Sekar Jagad motif performed the best, while the Truntum motif was the most challenging class due to its smooth and repetitive texture characteristics.
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References
Albakia, S. A. E., & Saputra, R. A. (2023). Identifikasi Jenis Daun Tanaman Obat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Model VGG16. Jurnal Informatika Polinema, 9(4), 451–460. https://doi.org/10.33795/jip.v9i4.1420
Bang Ritan, F. M., & Chandra, A. Y. (2025). Analisis Perbandigan Kinerja Model CNN Resnet-50, VGG19 dan Mobilenet dalam Klasifikasi Penyakit pada Tamnaman Mete. Jurnal Locus Penelitian dan Pengabdian, 4(8), 7903–7918. https://doi.org/10.58344/locus.v4i8.4261
Benita Suciani, A., Gunawansyah, G., & Maulana Adhinugraha, K. (2024). Klasifikasi Jenis Batik Menggunakan Algoritma K-Nearest Neighbors (KNN). Infotronik : Jurnal Teknologi Informasi dan Elektronika, 9(2), 83–89. https://doi.org/10.32897/infotronik.2024.9.2.3846
Dewi, S., Ramadhani, F., & Djasmayena, S. (2024). Klasifikasi Jenis Jerawat Berdasarkan Gambar Menggunakan Algoritma CNN (Convolutional Neural Network). Hello World Jurnal Ilmu Komputer, 3(2), 68–73. https://doi.org/10.56211/helloworld.v3i2.518
Dharmasisya, ", Fakultas, J., Zulfikar, R., & Putra, S. (2022). Upaya Pemerintah Terhadap Perlindungan Ekspresi Budaya Tradisional Batik Motif Parang Sebagai Warisan Budaya Dunia. Jurnal Program Magister Hukum FHUI, 1, 40.
Elzandy, I., Yulia Puspaningrum, E., & Lina Nurlaili, A. (2025). Klasifikasi Varientas Anggur Berdasarkan Citra Daun Menggunakan VGG16-XGBOOST. JATI (Jurnal Mahasiswa Teknik Informatika), 9(3), 5483–5491. https://doi.org/10.36040/jati.v9i3.14309
Filia, B. J., Lienardy, F. F., Laksana, I. K. P. B., Jordan, J. A., Siento, J. G., Honova, S. M., Hasana, S., & Permonangan, I. H. (2023). Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced Dataset. Procedia Computer Science, 227, 508–517. https://doi.org/10.1016/j.procs.2023.10.552
Hassa. (2024). Batik Sogan, Mengenal Sejarah, Asal, dan Makna Filosofinya. https://hassa.co.id/batik-sogan/
Jasmine, N. A. (2024, Oktober). Sejarah dan Asal-usul Batik, Warisan Budaya Indonesia yang Diakui UNESCO. https://www.detik.com/jogja/budaya/d-7568290/sejarah-dan-asal-usul-batik-warisan-budaya-indonesia-yang-diakui-unesco
Kumala, R. A., Sari, C. A., & Rachmawanto, E. H. (2025). A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification. Journal of Applied Informatics and Computing, 9(4), 1610–1624. https://doi.org/10.30871/jaic.v9i4.9958
Kurniawan, M. D., & Widagdo, J. (2024). Kajian Makna Dan Sejarah Desain Motif Truntum (Sebagai Media Pembelajaran Hidup). JurnalPendidikan Integrati, 5(3). https://ejurnals.com/ojs/index.php/jpi
Kusanti, J., Noersasongko, E., Soeleman, M. A., Alzami, F., Purwanto, & Hasibuan, Z. A. (2023). Pre-Processing Of Resize And Region Of Interest (ROI) To Improve The Accuracy Of Batik Detection Based On VGG-16. 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 242–247. https://doi.org/10.1109/EECSI59885.2023.10295666
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
Ma’ruf, M. T., Putra, E. D., Reswan, Y., & Juhardi, U. (2023). Classification Of Besurek Batik Fabrics Using Gray Level Co-Occurrence Matrix (GLCM) Features Extraction. Jurnal Komputer, Informasi dan Teknologi, 3(2). https://doi.org/10.53697/jkomitek.v3i2.1211
Meranggi, D. G. T., Yudistira, N., & Sari, Y. A. (2022). Batik Classification Using Convolutional Neural Network with Data Improvements. JOIV : International Journal on Informatics Visualization, 6(1), 6. https://doi.org/10.30630/joiv.6.1.716
Mienye, I. D., & Swart, T. G. (2024). A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications. Information, 15(12), 755. https://doi.org/10.3390/info15120755
Murinto, M., Rosyda, M., & Melany, M. (2023). Klasifikasi Jenis Biji Kopi Menggunkan Convolutional Neural Network dan Transfer Learning pada Model VGG16 dan MobileNetV2. JRST (Jurnal Riset Sains dan Teknologi), 7(2), 183. https://doi.org/10.30595/jrst.v7i2.16788
Nathani, S. (2024). A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models. https://doi.org/https://doi.org/10.48550/arXiv.2407.14576
Nikmah, F., Sukma, E. A., & Arjo, T. R. (2024). Rekaman Informasi Arsip Batik Berbasis Web. Polinema Press.
Rizky Rahmalia Ivani, A., Zulfi Kurniadi, A., Belia Auly Andira, A., Wahyuni, I., Studi Teknik Informatika, P., Teknologi dan Desain, F., & Teknologi dan Bisnis Asia Malang, I. (2025). Perbandiangan VGG16 dan MobileNetV2 untuk Klasifikasi Tingkat Kematangan Buah Apel. Jurnal Sistem Komputer Asia (JISKOMSIA), 3(1). https://doi.org/https://doi.org/10.32815/jiskomsia.v3i1.136
Tebo, B. P. S. K. (2025). Hari Batik Nasional 2025. https://tebokab.bps.go.id/id/news/2025/10/02/64/hari-batik-nasional-2025.html
UMKM, K. (2025). Semakin Diminati, Bisnis Batik Makin Prospektif. https://umkm.go.id/news/mcsossnwob44f3kw9mdp7cnz
Yu, F., Zhang, Q., Xiao, J., Ma, Y., Wang, M., Luan, R., Liu, X., Ping, Y., Nie, Y., Tao, Z., & Zhang, H. (2023). Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles. Remote Sensing, 15(12), 2988. https://doi.org/10.3390/rs15122988
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