Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network
DOI:
https://doi.org/10.35870/jtik.v9i3.3798Keywords:
CNN, Gabor Filter, Batik Classification, Deep Learning, Image ProcessingAbstract
This research aims to develop a batik motif classification system by utilizing Convolutional Neural Network (CNN) and Gabor Filter, in order to increase accuracy in texture feature extraction. The batik dataset used goes through a preprocessing stage, which includes normalization and data augmentation. During training, the model was tested with 10,000 iterations, using the Adam optimizer and the Categorical Cross-Entropy loss function, and evaluated via a confusion matrix. Test results show accuracy reaching 87%, with a precision and recall value of 90% each, and an F1-score of 89%. This method has proven effective for classifying batik motifs and has the potential to be applied in the fields of education, textile industry and cultural preservation.
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Atina, A. (2019). Aplikasi MATLAB pada teknologi pencitraan medis. Jurnal Penelitian Fisika dan Terapannya (JUPITER), 1(1), 28. https://doi.org/10.31851/jupiter.v1i1.3123.
Azmi, K., Defit, S., & Sumijan, S. (2023). Implementasi convolutional neural network (CNN) untuk klasifikasi batik tanah liat sumatera barat. Jurnal Unitek, 16(1), 28-40. https://doi.org/10.52072/unitek.v16i1.504.
Bariyah, T., & Rasyidi, M. A. (2021). Convolutional Neural Network Untuk Metode Klasifikasi Multi-Label Pada Motif Batik. Techno. Com, 20(1).
Bowo, T. A., Syaputra, H., & Akbar, M. (2020). Penerapan Algoritma Convolutional Neural Network Untuk Klasifikasi Motif Citra Batik Solo. Journal of Software Engineering Ampera, 1(2), 82-96. https://doi.org/10.51519/journalsea.v1i2.47.
Chen, Y., Zhu, L., Ghamisi, P., Jia, X., Li, G., & Tang, L. (2017). Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 14(12), 2355-2359. https://doi.org/10.1109/LGRS.2017.2764915.
Fahri, S., & Situmorang, S. (2024). Implementasi Metode Convolutional Neural Network (CNN) Dalam Klasifikasi Motif Batik. NUANSA INFORMATIKA, 18(1), 1-5. https://doi.org/10.25134/ilkom.v18i1.21.
Kudiya, K., & Atik, S. K. (2020). Kekuatan Desain Motif Batik Cirebon sebagai Ruang Identitas Indikasi Geografis Indonesia. Waca Cipta Ruang, 6(1), 1-12. https://doi.org/10.34010/wcr.v6i1.4192.
Leonardo, L. (2020). Penerapan metode filter Gabor untuk analisis fitur tekstur citra pada kain songket. Jurnal Sistem Komputer dan Informatika (JSON), 1(2), 120. https://doi.org/10.30865/json.v1i2.1942.
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), 229-236. https://doi.org/10.53697/jkomitek.v3i2.1211.
Maulana, F. (2021). Machine Learning Object Detection Tanaman Obat Secara Real-Time Menggunakan Metode Yolo (You Only Look Once). Skripsi p. Bandung: Sekolah Tinggi Manajemen Informatika & Ko.
Mawarni, D. I., Indarto, I., Deendarlianto, D., & Yuana, K. A. (2023). Metode Digital Image Processing Untuk Menentukan Distribusi Ukuran Diameter Gelembung Udara Pada Microgelembung Generator. Journal of Information System Management (JOISM), 4(2), 132-136. https://doi.org/10.24076/joism.2023v4i2.977.
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes classifier dan confusion matrix pada analisis sentimen berbasis teks pada Twitter. J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(2), 697-711.
Saifullah, S., Suryotomo, A. P., Dreżewski, R., Tanone, R., & Tundo, T. (2024). Optimizing brain tumor segmentation through CNN U-Net with CLAHE-HE image enhancement. In 2024 Conference Proceedings (pp. 90–101). https://doi.org/10.2991/978-94-6463-366-5_9.
Trixie, A. A. (2020). Filosofi motif batik sebagai identitas bangsa Indonesia. Folio, 1(1), 1-9. https://doi.org/10.37715/folio.v1i1.1380.
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.
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Copyright (c) 2025 Yudisman Ferdinan Bili, Tundo, Nandang Sutisna, Atsilah Daini Putri, Dita Tri Yuliantoro, Laily Nurmayanti
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