THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH
The aimed of this study was to apply the feature extraction method of GLCM and Back-propagation Artificial Neural Network (ANN) to classify Lombok's typical Songket woven cloth by classifying based on the texture of the Songket woven cloth. Songket woven cloth in Lombok in terms of weaving and texture are vary from region to region. For example the songket woven cloth in Pringgasela Village, Sukarara Village and Sade Village has differences in texture and motifs. For this reason, this study focuses on classifying Lombok's typical Songket woven cloth by performing feature extraction on woven cloth using the GLCM method and the classification method uses Back-propagation Artificial Neural Network (ANN). For data collection, the data was taken directly from the Songket weaving centers in Pringgasela, Sade and Sukarara. In the classification stage the training data used were 64 data and 11 test data. Then the epoch used was 41 iterations with a time of 0:00:04, with neurons 80 and 100. The use of neurons 80 generated 18% which was successful in the classification. While using 100 neurons generated 100% successful which was can be classified. Based on the classification results obtained, the use of 100 neurons gained good classification results.
A. Kasim, A., & Harjoko, A. (2014). Klasifikasi Citra Batik Menggunakan Jaringan Syaraf Tiruan Berdasarkan Gray Level Co-Occurrence Matrices (GLCM). Seminar Nasional Aplikasi Teknologi Informasi, 1(1), 7–13. https://journal.uii.ac.id/Snati/article/view/3256
Amalia, I. (2018). Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM). Jurnal Infomedia, 3(2), 64–68.
Gressiva., and Chandra, F. (2018). Sistem Pengenalan Motif Songket Melayu Menggunakan Ekstraksi Fitur Principal Component Analysisdan Gray Level Co-Occurence Matrix dan Jaringan Saraf Tiruan. Jom FTEKNIK, 5(2), 1–7. https://jom.unri.ac.id/index.php/JOMFTEKNIK/article/view/22206
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. American Journal of Neural Networks and Applications, 5(1), 23–27.
Imran, B., & Efendi, M. M. (2020). Laporan Penelitian Akhir Mandiri: The Implementation Of Extraction Feature Using Glcm And Back-Propagation Artificial Neural Network To Clasify Lombok Songket Woven Cloth.
Imran, B., Gunawan, K., Zohri, M., & Bakti, L. D. (2018). Fingerprint Pattern of Matching Family with GLCM Feature. Telkomnika, 16(4), 1864–1869.
Mauko, I, C., and Lukmetlabla, N, M, S. (2016). Pengenalan pola citra digital motif kain tenun alor menggunakan metode transformasi wavelet dan adaptive neuro-fuzzy Inference system. Seminar Nasional Sainstek, 51–57.
Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3(5), 290–294. http://www.ijsrp.org/research-paper-0513/ijsrp-p1750.pdf
Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Electronics and Instrumentation Engineering, 2(9), 4206–4212.
Sari, Y. (2018). Klasifikasi Pengenalan Motif Batik Berbasis Image Retrival. Jurnal Teknik Lingkungan, 4(2), 27–33. https://ppjp.ulm.ac.id/journal/index.php/jukung/article/view/6581
Setiohardjo, N. M. (2013). Texture Analysis for Fabric Motif Classification (Case Study: Nusa Tenggara Timur Woven Fabric). Indonesian Journal of Computing and Cybernetics Systems, 8(2), 177–188.
Soesanti, I. (2015). Klasifikasi dan pengenalan pola batik berbasis ciri statistis. CITEE 2015, 304–309. http://citee.ft.ugm.ac.id/2015/proceeding/download51.php?f=Indah Soesanti - Klasifikasi dan Pengenalan Pola.pdf
Suharjito, S., Imran, B., & Girsang, A. S. (2017). Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint. International Journal of Electrical and Computer Engineering (IJECE), 7(5), 2738–2745. https://doi.org/10.11591/ijece.v7i5
Surya, R. A., Fadlil, A., & Yudhana, A. (2017). Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk Klasifikasi citra Batik Pekalongan. Jurnal Informatika: Jurnal Pengembangan IT, 2(2), 23–26.
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