Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Sudi Al Sasongko - University of Mataram, Mataram, 83125, Indonesia
Erni Jayanti - University of Mataram, Mataram, 83125, Indonesia
Suthami Ariessaputra - University of Mataram, Mataram, 83125, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.6.4.1532

Abstract


Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.


Keywords


Songket; Gray Level Co-Occurrence Matrix (GLCM); neural network; backpropagation.

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References


L. Leonardo, “Penerapan Metode Filter Gabor Untuk Analisis Fitur Tekstur Citra Pada Kain Songket,†J. Sist. Komput. dan Inform., vol. 1, no. 2, 2020, doi: 10.30865/json.v1i2.1942.

Y. Y, M. R. Yusof, and Y. Ibrahim, “Songket : the Linkage Between Heritage and Tourism in Malaysia,†Asian People J., vol. 1, no. 2, 2018.

H. Hambali, M. Mahayadi, and ..., “Classification of Lombok Songket Cloth Image Using Convolution Neural Network Method (Cnn),†Pilar Nusa Mandiri …, no. 85, pp. 149–156, 2021, doi: 10.33480/pilar.v17i2.2705.

B. Imran and M. M. Efendi, “The Implementation of Extraction Feature Using GLCM and Back-Propagation Artificial Neural Network to Clasify Lombok Songket Woven Cloth,†J. Techno Nusa Mandiri, vol. 17, no. 2, 2020, doi: 10.33480/techno.v17i2.1680.

M. Janpourtaher, “Scientific approach of preservation treatment and restoration procedures on historical royal Songket Sarong,†Int. J. Conserv. Sci., vol. 10, no. 1, 2019.

A. C. Siregar and B. C. Octariadi, “Classification of Sambas Traditional Fabric ‘Kain Lunggi’ Using Texture Feature,†IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, 2019, doi: 10.22146/ijccs.49782.

F. Wijayanti, T. Rohendi Rohidi, and K. Utara, “Palembang Songket Fabric Visual Motif,†Cathar. J. Arts Educ., vol. 8, no. 4, pp. 429–436, 2019.

H. Fonda, “Klasifikasi Batik Riau dengan Menggunakan Convolutional Neural Networks (CNN),†J. Ilmu Komput., vol. 9, no. 1, 2020, doi: 10.33060/jik/2020/vol9.iss1.144.

F. T. Maharani and Z. Lynch, “The Implementation of the POPMAR (Policy, Organising, Planning and Implementing, Measuring Performance, Audit and Reviewing) Model in Occupational Health and Safety Risk Management in an Indonesian Batik Company,†Indones. J. Occup. Saf. Heal., vol. 10, no. 3, 2021, doi: 10.20473/ijosh.v10i3.2021.420-432.

H. Warsono, T. Afrizal, and R. J. Pinem, “Conserving heritage craft in meeting contemporary and demand issues: A collaborative governance approach,†Int. J. Entrep., vol. 25, no. 1, 2021.

J. Junaidi, N. Pramestie Wulandari, and D. Hamdani, “Subahnale dan Rang-rang Pembelajaran Matematika SMP,†Griya J. Math. Educ. Appl., vol. 1, no. 4, 2021, doi: 10.29303/griya.v1i4.102.

R. C. I. Prahmana and U. D’Ambrosio, “Learning geometry and values from patterns: Ethnomathematics on the batik patterns of yogyakarta, indonesia,†J. Math. Educ., vol. 11, no. 3, 2020, doi: 10.22342/jme.11.3.12949.439-456.

S. Devella, Y. Yohannes, and F. N. Rahmawati, “Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT,†JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 310–320, Aug. 2020, doi: 10.35957/jatisi.v7i2.289.

Y. Yohannes, S. Devella, and A. H. Pandrean, “Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang,†J. Tek. Inform. dan Sist. Inf., vol. 5, no. 3, Jan. 2020, doi: 10.28932/jutisi.v5i3.1978.

M. A. Rasyidi, R. Handayani, and F. Aziz, “Identification of batik making method from images using convolutional neural network with limited amount of data,†Bull. Electr. Eng. Informatics, vol. 10, no. 3, 2021, doi: 10.11591/eei.v10i3.3035.

Z. E. Fitri, A. Madjid, and M. Nanda, “Penerapan Neural Network untuk Klasifikasi Kerusakan Mutu Tomat,†J. Rekayasa Elektr., vol. 16, no. 1, pp. 44–49, 2020, doi: 10.17529/jre.v16i1.15535.

J. M. Challab and F. Mardukhi, “A hybrid method based on LSTM and optimized SVM for diagnosis of novel coronavirus (COVID-19),†Trait. du Signal, vol. 38, no. 4, pp. 1061–1069, Aug. 2021, doi: 10.18280/ts.380416.

R. S. Patil and N. Biradar, “Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network,†Evol. Intell., vol. 14, no. 4, pp. 1459–1474, 2021, doi: 10.1007/s12065-020-00403-x.

Y. Park and J. M. Guldmann, “Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?,†Ecol. Indic., vol. 109, Feb. 2020, doi: 10.1016/j.ecolind.2019.105802.

I. Amalia, “Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM),†J. Infomedia, vol. 3, no. 2, 2018, doi: 10.30811/jim.v3i2.715.

M. Sholihin, “Classification of Batik Lamongan Based on Features of Color, Texture and Shape,†Kursor, vol. 9, no. 1, 2018, doi: 10.28961/kursor.v9i1.114.

I. P. G. S. Andisana, M. Sudarma, and I. M. O. Widyantara, “Pengenalan Dan Klasifikasi Citra Tekstil Tradisional Berbasis Web Menggunakan Deteksi Tepi Canny, Local Color Histogram Dan Co-Occurrence Matrix,†Maj. Ilm. Teknol. Elektro, vol. 17, no. 3, 2018, doi: 10.24843/mite.2018.v17i03.p15.

C. Jatmoko and D. Sinaga, “A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor,†J. Appl. Intell. Syst., vol. 3, no. 2, 2019, doi: 10.33633/jais.v3i2.2151.

N. Nurhalimah, I. G. P. Suta Wijaya, and F. Bimantoro, “Klasifikasi Kain Songket Lombok Berdasarkan Fitur GLCM dan Moment Invariant Dengan Teknik Pengklasifikasian Linear Discriminant Analysis (LDA),†J. Teknol. Informasi, Komputer, dan Apl. (JTIKA ), vol. 2, no. 2, 2020, doi: 10.29303/jtika.v2i2.98.

I. Nurhaida, V. Ayumi, D. Fitrianah, R. A. M. Zen, H. Noprisson, and H. Wei, “Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition,†Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 2045–2053, 2020, doi: 10.11591/ijece.v10i2.pp2045-2053.

A. Zahra, E. Ernawati, and E. P. Purwandari, “Perbandingan Metode K-Means Clustering dan Discrete Cosine Transform Untuk Kompresi Citra Batik Besurek Motif Gabungan,†Pseudocode, vol. 5, no. 2, 2018, doi: 10.33369/pseudocode.5.2.46-55.

Z. Xing and H. Jia, “Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm,†IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2904511.

K. Chandraprabha and S. Akila, “Texture Feature Extraction for Batik Images Using GLCM and GLRLM with Neural Network Classification,†Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., 2019, doi: 10.32628/cseit195322.

O. Sudana, I. P. A. Bayupati, and D. G. Yudiana, “Classification of Maturity Level of the Mangosteen using the Convolutional Neural Network (CNN) Method,†Int. J. Adv. Sci. Technol., vol. 135 (20), no. February, 2020, doi: 10.33832/ijast.2020.135.04.

F. A. Ahda and F. A. Rahman, “Perancangan Infografis Songket Lombok,†J. Desain Komun. Vis. Asia, vol. 1, no. 1, 2017, doi: 10.32815/jeskovsia.v1i1.307.

R. Aprianti, K. Evandari, R. A. Pramunendar, and M. Soeleman, “Comparison of Classification Method on Lombok Songket Woven Fabric Based on Histogram Feature,†2021, doi: 10.1109/iSemantic52711.2021.9573223.

P. N. Andono and E. H. Rachmawanto, “Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, 2021, doi: 10.29207/resti.v5i1.2615.