Improvement Deep Learning Model for Batik Classification Used EfficientNetB2 and Augmentation Data
DOI:
https://doi.org/10.52436/1.jutif.2026.7.4.4902Keywords:
Augmentasi, Batik, Classification, Deep Learning, EfficientNetB2Abstract
Batik is one of Indonesia’s cultural heritages that has been recognized by UNESCO. The diversity of batik motifs in Indonesia presents a challenge in classifying each motif from different regions. The purpose of this research is to improve batik motif classification using a deep learning model with the Convolutional Neural Network EfficientNetB2 architecture combined with data augmentation techniques. This study uses a dataset from Kaggle consisting of 1,964 images with seven batik motifs from the northern coast of Java: buketan, jlamprang, liong, mega mendung, negative, singa barong, and tujuh rupa. The augmentation technique applied in this study is traditional augmentation, including transformations such as horizontal flipping, rotation, and brightness adjustment. This was implemented to overcome dataset limitations, reduce the risk of overfitting, and increase dataset variation. The model integrates transfer learning through ImageNet pre-training to optimize the modification of the EfficientNetB2 architecture for specific batik motifs. EfficientNetB2 achieved an accuracy of 99% on the validation data, with precision, recall, and F1-scores consistently above 98%. This result is higher compared to previous research that applied the MPSO technique, which achieved 94% accuracy with precision, recall, and F1-scores of 78%. Model performance evaluation was conducted using a confusion matrix. Experimental results show that minimal misclassification occurred on the negative motif, with three samples incorrectly classified as jlamprang. These findings indicate that EfficientNetB2 combined with data augmentation significantly improves classification accuracy. Moreover, the use of the EfficientNetB2 architecture does not require long computational time, making it highly practical and efficient. This research contributes to the field of machine learning, the preservation of batik culture, and has the potential to support the batik industry in the commercial sector.
Downloads
References
R. F. Alya, M. Wibowo, and P. Paradise, “Classification of Batik Motif Using Transfer Learning on Convolutional Neural Network (Cnn),” J. Tek. Inform., vol. 4, no. 1, pp. 161–170, 2023, doi: 10.52436/1.jutif.2023.4.1.564.
A. Prayoga, Maimunah, P. Sukmasetya, Muhammad Resa Arif Yudianto, and Rofi Abul Hasani, “Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta,” J. Appl. Comput. Sci. Technol., vol. 4, no. 2, pp. 82–89, 2023, doi: 10.52158/jacost.v4i2.486.
S. Aras, A. Setyanto, and Rismayani, “Deep Learning Untuk Klasifikasi Motif Batik Papua Menggunakan EfficientNet dan Trasnfer Learning,” Insect (Informatics Secur. J. Tek. Inform., vol. 8, no. 1, pp. 11–20, 2022, doi: 10.33506/insect.v8i1.1865.
D. G. T. Meranggi, N. Yudistira, and Y. A. Sari, “Batik Classification Using Convolutional Neural Network with Data Improvements,” Int. J. Informatics Vis., vol. 6, no. 1, pp. 6–11, 2022, doi: 10.30630/joiv.6.1.716.
A. E. Minarno, I. Soesanti, and A. Nugroho, “INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Batik Classification using Microstructure Co-occurrence Histogram,” vol. 8, no. March, pp. 134–140, 2024, [Online]. Available: www.joiv.org/index.php/joiv DOI: http://dx.doi.org/10.62527/joiv.8.1.2152
Z. Y. Lamasigi, “DCT Untuk Ekstraksi Fitur Berbasis GLCM Pada Identifikasi Batik Menggunakan K-NN,” vol. 3, pp. 1–6, 2021. https://doi.org/10.33096/ilkom.v13i3.1025.322-333
Bagus Untung Saputra, Gunawan, and Wresti Andriani, “Pengenalan Motif Batik Pesisir Pulau Jawa Menggunakan Convolutional Neural Network,” Nuansa Inform., vol. 17, no. 2, pp. 119–125, 2023, doi: 10.25134/ilkom.v17i2.32.
D. I. Aryani and E. Djakaria, “Penerapan Motif Batik Pesisir Utara Jawa pada Perhiasan Logam (Studi Kasus: Warak Ngendog),” J. Desain Idea J. Desain Prod. Ind. Inst. Teknol. Sepuluh Nop. Surabaya, vol. 20, no. 2, p. 80, 2021, doi: 10.12962/iptek_desain.v20i2.11605.
T. B. Sasongko, H. Haryoko, and A. Amrullah, “Analisis Efek Augmentasi Dataset dan Fine Tune pada Algoritma Pre-Trained Convolutional Neural Network (CNN),” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 4, pp. 763–768, 2023, doi: 10.25126/jtiik.20241046583.
E. Khoirunnisa, F. Alzami, R. A. Pramunendar, and R. A. Megantara, “Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation,” vol. 9, no. 1, pp. 43–54, 2025. doi:10.33395/sinkron. v9i1.14308
Ahmad Darman Huri, Rizal Arya Suseno, and Yufis Azhar, “Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 6, pp. 952–957, 2022, doi: 10.29207/resti.v6i6.4357.
O. A. Abioye, S. Thomas, C. R. Odimba, and A. J. Olalekan, “Generic hybrid model for breast cancer mammography image classification using EfficientNetB2,” Dutse J. Pure Appl. Sci., vol. 9, no. 3b, pp. 281–289, 2023, doi: 10.4314/dujopas.v9i3b.30.
R. Artikel, A. I. Rasidi, Y. Al, H. Pasaribu, A. Ziqri, and F. D. Adhinata, “Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network,” vol. 8, no. April, pp. 142–149, 2022. doi: 10.4314/dujopas.v9i3b.30.
R. Andrian, R. Taufik, D. Kurniawan, A. S. Nahri, and H. C. Herwanto, “Lampung Batik Classification Using AlexNet , EfficientNet , LeNet and MobileNet Architecture,” vol. 15, no. 11, pp. 930–935, 2024. DOI: https://doi.org/10.47065/bulletincsr.v6i1.863
H. Zhao et al., “Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI,” Heliyon, vol. 10, no. 15, p. e35115, 2024, doi: 10.1016/j.heliyon.2024.e35115. DOI: https://doi.org/10.35882/jeeemi.v8i1.1406
U. Khasanah, “Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification,” pp. 20–24. DOI:10.1109/CITSM50537.2020.9268890
J. Witjaksono, M. Y. Pusadan, Y. Anshori, and R. Ardiansyah, “Klasifikasi jenis batik bomba menggunakan convolutional neural network dengan arsitektur efficient-net b2 (batik bomba sulawesi tengah ),” vol. 10, no. 3, pp. 2134–2147, 2025. DOI: https://doi.org/10.29100/jipi.v10i3.6191
S. Ariessaputra, V. H. Vidiasari, S. M. Al Sasongko, B. Darmawan, and S. Nababan, “Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm,” Int. J. Informatics Vis., vol. 8, no. 1, pp. 38–44, 2024, doi: 10.62527/joiv.8.1.1386.
L. Elvitaria, E. F. Ahmad, N. A. Samsudin, S. K. A. Khalid, Salamun, and Z. Indra, “An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification,” Int. J. Informatics Vis., vol. 9, no. 1, pp. 115–119, 2025, doi: 10.62527/joiv.9.1.2591.
Murinto and S. Winiarti, “Modified particle swarm optimization (MPSO) optimized CNN’s hyperparameters for classification,” Int. J. Adv. Intell. Informatics, vol. 11, no. 1, pp. 133–142, 2025, doi: 10.26555/ijain.v11i1.1303.
U. Pembangunan, N. Veteran, J. Timur, J. R. Madya, and G. Anyar, “PENERAPAN ARSITEKTUR CNN-EFFICIENTNETB2 DENGAN TRANSFER LEARNING PADA KLASIFIKASI GAMBAR TOKOH WAYANG KULIT,” vol. 13, no. 1, 2025. DOI:
https://doi.org/10.23960/jitet.v13i1.5626
I. Print, I. Online, L. Hakim, H. R. Rahmanto, S. P. Kristanto, and D. Yusuf, “Volume 17 , Nomor 1 , Januari 2023 , Page 203-211 KLASIFIKASI CITRA MOTIF BATIK BANYUWANGI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” vol. 17, pp. 203–211, 2023. DOI:10.33365/jti.v17i1.2342
S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Comput. Biol. Med., vol. 111, no. March, p. 103345, 2019, doi: 10.1016/j.compbiomed.2019.103345.
H. Sastypratiwi, H. Muhardi, and Yulianti, “Batik Recognition and Classification Using Transfer Learning and MobileNet Approach,” Int. J. Informatics Vis., vol. 8, no. 4, pp. 2400–2410, 2024, doi: 10.62527/joiv.8.4.2407.
D. Ardianto, J. Jasril, S. Sanjaya, L. Handayani, and F. Syafria, “Klasifikasi Citra Daging Sapi dan Babi Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur EfficientNet-B2 dan Augmentasi Data,” J. Inform. Univ. Pamulang, vol. 8, no. 2, pp. 153–164, 2023, doi: 10.32493/informatika.v8i2.30587.
N. M. K. K. Handayani, E. Y. Hidayat, M. Naufal, and P. L. W. E. Putra, “Pengenalan Ekspresi Wajah Menggunakan Transfer Learning MobileNetV2 dan EfficientNet-B0 dalam Memprediksi Perkelahian,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 106, 2024, doi: 10.30865/mib.v8i1.7048.
B. J. Filia et al., “Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced Dataset,” Procedia Comput. Sci., vol. 227, pp. 508–517, 2023, doi: 10.1016/j.procs.2023.10.552.
U. Muhammadiyah, P. Hamka, and D. K. I. Jakarta, “KOMPARASI MOBILENETV2 DENGAN KUSTOMISASI TRANSFER LEARNING DAN HYPERPARAMETER UNTUK IDENTIFIKASI TUMOR OTAK COMPARISON OF MOBILENETV2 WITH CUSTOMISED TRANSFER LEARNING AND HYPERPARAMETER FOR BRAIN TUMOUR IDENTIFICATION,” vol. 12, no. 1, pp. 229–240, 2025, doi: 10.25126/jtiik.2025129582.
M. B. Kurniwan, E. Utami, and U. A. Yogyakarta, “COMPARATIVE ANALYSIS OF CONTRAST ENHANCEMENT METHODS FOR CLASSIFICATION OF PEKALONGAN BATIK MOTIFS USING CONVOLUTIONAL NEURAL NETWORK,” vol. 5, no. 6, pp. 1779–1787, 2024. DOI: https://doi.org/10.52436/1.jutif.2024.5.6.2621
V. M. Risma and E. Utami, “Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques,” vol. 6, no. 2, pp. 981–994, 2025. DOI: https://doi.org/10.52436/1.jutif.2025.6.2.4315
D. Andrianto, R. Prathivi, and M. Liu, “COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING,” vol. 5, no. 6, pp. 1833–1838, 2024. https://doi.org/10.52436/1.jutif.2024.5.6.3477
Tri Wahyu Qur’ana, “Implementasi Metode Convolutional Neural Network (CNN) untuk Klasifikasi Motif pada Citra Sasirangan,” Media Inform., vol. 7, no. 2, p. 10, 2023, [Online]. Available:http://jurnal.big.go.id/index.php/GM/article/view/810DOI: https://doi.org/10.59934/jaiea.v4i2.900
A. A. A. El-Aziz, M. Elmogy, and S. A. El-Ghany, “A robust tuned EfficientNet-B2 using dynamic learning for predicting different grades of brain cancer,” Egypt. Informatics J., vol. 30, no. January, p. 100694, 2025, doi: 10.1016/j.eij.2025.100694.
M. A. Rasyidi, R. Handayani, and F. Aziz, “Identification of batik making method from images using convolutional neural network with limited amount of data,” Bull. Electr. Eng. Informatics, vol. 10, no. 3, pp. 1300–1307, 2021, doi: 10.11591/eei.v10i3.3035.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ismi Kusumaningroem, M.Arief Soeleman, Fikri Budiman, M. Hasan Nafi
This work is licensed under a Creative Commons Attribution 4.0 International License.
</a
