Application of Convolutional Neural Network in Malay Woven Fabric Pattern Image Classification
DOI:
https://doi.org/10.37859/coscitech.v5i1.6713
Abstract
The use of electronic computerized media is growing along with advances in hardware and software as an analytical tool with various algorithms and methods for classifying and measuring objects in various contexts. This progress aims to overcome the weaknesses that exist in conventional methods used in the identification process. The identification process can be applied to various objects, one of which is an image object. An image is a visual representation of an object formed through a combination of RGB (red, green, blue) colors. RGB color components or features have a range of values from 0 to 255 in an image. Weaving is a type of fabric that is specially made with distinctive motifs. Malay weaving motifs have a lot of diversity, this diversity makes it difficult to distinguish the motifs of these fabrics.This study aims to recognize and distinguish the pattern of Malay woven fabric. The method used in this research is Convolutional Neural Network (CNN). The CNN method has several stages, namely Convolution Layer, Pooling Layer, Rectifed Linear Unit (ReLU) Function, Fully-Connected Layer, Transfer Learning, Optimizer and Accuracy. The dataset used in this research is sourced from Tenun Putri Mas Bengkalis. The dataset used consists of 1000 images of weaving motifs which are divided into 80% training data and 20% testing data, from the existing dataset divided into three categories of weaving motifs namely pucuk rebung, elbow clouds and elbow keluang. The results in this study are considered good because they produce accuracy with a result of 95% with an epoch value of 15. From the results of good enough accuracy, it is hoped that it can help the community in recognizing Malay weaving motifs.
Downloads
References
[2] R. Hayami, S. Mohnica, and Soni, “Klasifikasi multilabel komentar toxic pada sosial media twitter menggunakan Convolutional Neural Network(CNN),” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 1, pp. 1–6, 2023, doi: 10.37859/coscitech.v4i1.4365.
[3] R. Firdaus, Joni Satria, and B. Baidarus, “Klasifikasi Jenis Kelamin Berdasarkan Gambar Mata Menggunakan Algoritma Convolutional Neural Network (CNN),” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 3, pp. 267–273, 2022, doi: 10.37859/coscitech.v3i3.4360.
[4] W. Styorini, W. E. Putra, W. Khabzli, and D. Y. Triyani, “Jurnal Politeknik Caltex Riau Penerapan Deep Learning Pada Jenis Penyakit Tanaman Kelapa Sawit Menggunakan Algoritma Convolutional Neural Network,” J. Komput. Terap., vol. 8, no. 2, pp. 359–367, 2022, [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/
[5] M. Malika and E. Widodo, “Implementasi Deep Learning Untuk Klasifikasi Gambar Menggunakan Convolutional Neural Network (Cnn) Pada Batik Sasambo,” Pattimura Proceeding Conf. Sci. Technol., pp. 335–340, 2022, doi: 10.30598/pattimurasci.2021.knmxx.335-340.
[6] A. TiaraSari and E. Haryatmi, “Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 265–271, 2021, doi: 10.29207/resti.v5i2.3040.
[7] S. Winiarti, M. Y. A. Saputro, and S. Sunardi, “Deep Learning dalam Mengindetifikasi Jenis Bangunan Heritage dengan Algoritma Convolutional Neural Network,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 831, 2021, doi: 10.30865/mib.v5i3.3058.
[8] Y. Rizki, R. Medikawati Taufiq, H. Mukhtar, and D. Putri, “Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN,” IT J. Res. Dev., vol. 5, no. 2, pp. 215–225, 2021, doi: 10.25299/itjrd.2021.vol5(2).5831.
[9] E. Oktafanda, “Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN),” J. Inform. Ekon. Bisnis, vol. 4, no. 3, pp. 72–77, 2022, doi: 10.37034/infeb.v4i3.143.
[10] R. Aryanto, M. Alfan Rosid, and S. Busono, “Penerapan Deep Learning untuk Pengenalan Tulisan Tangan Bahasa Aksara Lota Ende dengan Menggunakan Metode Convolutional Neural Networks,” J. Inf. dan Teknol., vol. 5, no. 1, pp. 258–264, 2023, doi: 10.37034/jidt.v5i1.313.
[11] Ayu Ratna Juwita, Tohirn Al Mudzakir, Adi Rizky Pratama, Purwani Husodo, and Rahmat Sulaiman, “Identifikasi Citra Batik Dengan Metode Convolutional Neural Network,” Buana Ilmu, vol. 6, no. 1, pp. 192–208, 2021, doi: 10.36805/bi.v6i1.1996.
[12] I. P. Putra, R. Rusbandi, and D. Alamsyah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network,” J. Algoritm., vol. 2, no. 2, pp. 102–112, 2022, doi: 10.35957/algoritme.v2i2.2360.
[13] W. Ouyang and P. Zhu, “A Lightweight Convolutional Neural Network Method for Image Classification,” Proc. - 2022 2nd Int. Conf. Front. Electron. Inf. Comput. Technol. ICFEICT 2022, pp. 410–415, 2022, doi: 10.1109/ICFEICT57213.2022.00079.
[14] F. Y. Tember, I. Najiyah, T. Informatika, F. T. Informasi, and J. Barat, “Klasifikasi Motif Batik Jawa Barat menggunakan Convolutional Neural Network Classification of West Java Batik Motifs Using Convolutional Neural Network,” vol. 12, pp. 282–292, 2023.
