CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN)

  • Hambali Hambali (1*) Universitas Teknologi Mataram
  • Mahayadi Mahayadi (2)
  • Bahtiar Imran (3)

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v17i2.2705

Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth

Abstract

The diversity of tribes makes Indonesia rich in culture that characterizes it, one of which is traditional cloth. Through a variety of patterns and motifs that exist in traditional fabrics, reflecting the life, customs, and culture that exist in an area. Lombok is one of the areas that produces a typical songket cloth. The famous songket craft centers in Lombok are located in the Pringgasela area, Pringgasela District, Sade Village is in Pujut District, Central Lombok Regency and Sukarara is in Jonggat District, Central Lombok Regency. Each area of ​​the center for songket craftsmen has their own characteristics both in terms of the name, motif and texture. When viewed with the naked eye, the texture of each songket will look the same, to be able to know the differences in the texture of each songket, it is necessary to do a classification using computers or technology. Today's society still does not know much information about the textures of songket cloth. The method used to classify the typical Lombok songket in this study uses the Convolution Neural Network (CNN) method. The results obtained from the use of 64 datasets, with details of 40 types of Sade songket and 24 types of Pringgasela songket, after the dataset is trained it produces 86.36% accuracy, 87% precision, 86% recall, and 86% F1-Score.

 

Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth.

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References

Amalia, I. (2018). Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM). Jurnal Infomedia, 3(2), 64–68. https://doi.org/10.30811/jim.v3i2.715

Azhar, R., Tuwohingide, D., Kamudi, D., Sarimuddin, & Suciati, N. (2015). Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine. Procedia Computer Science, 72, 24–30. https://doi.org/10.1016/j.procs.2015.12.101

Fonda, H. (2020). Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn). Jurnal Ilmu Komputer, 9(1), 7–10. https://doi.org/10.33060/jik/2020/vol9.iss1.144

Harani, N. H., Prianto, C., & Hasanah, M. (2019). Deteksi Objek Dan Pengenalan Karakter Plat Nomor Kendaraan Indonesia Menggunakan Metode Convolutional Neural Network ( CNN ) Berbasis Python. Jurnal Teknik Informatika, 11(3), 47–53.

Imran, B. (2019). Content-Based Image Retrieval Based on Texture and Color Combinations Using Tamura Texture Features and Gabor Texture Methods. American Journal of Neural Networks and Applications, 5(1), 23. https://doi.org/10.11648/j.ajnna.20190501.14

Imran, B., & Efendi, M. M. (2020). THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH. Jurnal Techno Nusa Mandiri, 17(2), 131–136.

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

Mawan, R. (2020). Klasifikasi motif batik menggunakan Convolutional Neural Network. Jnanaloka, 45–50. https://doi.org/10.36802/jnanaloka.2020.v1-no1-2

Minarno, A. E., Kurniawardhani, A., & Bimantoro, F. (2016). Image retrieval based on multi structure co-occurrence descriptor. Telkomnika (Telecommunication Computing Electronics and Control), 14(3), 1175–1182. https://doi.org/10.12928/TELKOMNIKA.v14i3.3292

Putu Aryasuta Wicaksana, I Made Sudarma, D. C. K. (2019). Pengenalan Pola Motif Kain Tenun Gringsing Menggunakan Metode Convolutional Neural Network Dengan Model Arsitektur. Jurnal SPEKTRUM, 6(3), 159–168.

Salawazo, V. M. P., Gea, D. P. J., Gea, R. F., & Azmi, F. (2019). Implementasi Metode Convolutional Neural Network ( CNN ) Pada Peneganalan Objek Video Cctv. Jurnal Mantik Penusa, 3(1), 74–79.

Setiohardjo, N. M., & Harjoko, A. (2014). Analisis Tekstur untuk Klasifikasi Motif Kain (Studi Kasus Kain Tenun Nusa Tenggara Timur). IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 10(1), 177. https://doi.org/10.22146/ijccs.6545

Willyanto, A., Alamsyah, D., & Irsyad, H. (2021). Identifikasi Tulisan Tangan Aksara Jepang Hiragana Menggunakan Metode CNN Arsitektur VGG-16. 2(1), 1–11.

Yuhandri, Madenda, S., Wibowo, E. P., & Karmilasari, karmila@staff gunadarma ac id. (2017). Object feature extraction of songket image using chain code algorithm. International Journal on Advanced Science, Engineering and Information Technology, 7(1), 235–241. https://doi.org/10.18517/ijaseit.7.1.1479

Yuhandri, Y. (2019). Perbandingan Metode Cropping Pada Sebuah Citra Untuk Pengambilan Motif Tertentu Pada Kain Songket Sumatera Barat. Komtekinfo, 6(1), 95–105. https://doi.org/10.35134/komtekinfo.v6i1.273

Published
2021-09-08
How to Cite
Hambali, H., Mahayadi, M., & Imran, B. (2021). CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN). Jurnal Pilar Nusa Mandiri, 17(2), 149-156. https://doi.org/10.33480/pilar.v17i2.2705
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