Published: 2025-07-01
Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network
DOI: 10.35870/jtik.v9i3.3798
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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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