Published: 2025-07-01
Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network
DOI: 10.35870/jtik.v9i3.3798
Yudisman Ferdian Bili, Tundo, Nandang Sutisna, Atsilah Daini Putri, Dita Tri Yuliantoro, Laily Nurmayanti
- Yudisman Ferdian Bili: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Indonesia
- Tundo: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , , Indonesia
- Nandang Sutisna: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , , Indonesia
- Atsilah Daini Putri: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , , Indonesia
- Dita Tri Yuliantoro: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , , Indonesia
- Laily Nurmayanti: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , , Indonesia
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Abstract
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.
Keywords
CNN ; Gabor Filter ; Batik Classification ; Deep Learning ; Image Processing
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 3 (2025)
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Section: Computer & Communication Science
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i3.3798
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Yudisman Ferdian Bili
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Tundo
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Nandang Sutisna
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Atsilah Daini Putri
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Dita Tri Yuliantoro
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Laily Nurmayanti
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
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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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