Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik

Keywords: Batik, classification, SVM, KNN, feature extraction

Abstract

Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.

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References

T. Handhayani, J. Hendryli, and L. Hiryanto, "Comparison of shallow and deep learning models for classification of Lasem batik patterns," in Proc. - 2017 1st Int. Conf. Informatics Comput. Sci. ICICoS 2017, Semarang, 2018.

Ardianti, Syaripudin, and Y. A. Gerhana, "Klasifikasi Motif Batik Lampung Menggunakan Ekstraksi Ciri Tepi Canny dan Algoritma Naive Bayes Classifier," Insight, vol. 1, no. 1, pp. 96–102, 2018.

Candra Irawan, Ericha Nurvia Ardyastiti, De Rosal Ignatius Moses Setiadi, Eko Hari Rachmawanto, and Christy Atika Sari, "A Survey: Effect of the Number of GLCM Features on Classification Accuracy of Lasem Batik Images using K-Nearest Neighbor," in 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, 2019.

R. Wiryadinata, M. R. Adli, R. Fahrizal, and R. Alfanz, "Klasifikasi 12 Motif Batik Banten Menggunakan Support Vector Machine," Jurnal EECCIS, vol. 13, no. 1, pp. 60-64, 2019.

Annisa Handayani, Ade Jamal, and Ali Akbar Septiandri, "Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara ," Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 6, no. 4, pp. 394-403, 2017.

Rizal Fikri, Fitri Arnia, and Rusdha Muharar, "Pengenalan Karakter Tulisan Tangan Jawi Menggunakan Metode New Relative Context dan SVM," Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 5, no. 3, pp. 233-238, 2016.

Rakesh Asery, Ramesh Kumar Sunkaria, Lakhan Dev Sharma, and Aman Kumar, "Fog detection using GLCM based features and SVM," in 2016 Conference on Advances in Signal Processing (CASP), Pune, India, 2016.

Gunjan Mukherjee, Arpitam Chatterjee, and Bipan Tudu, "Study on the potential of combined GLCM features towards medicinal plant classification," in 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC), Kolkata, India, 2016.

Krishna Chaitanya Tatikonda, Chandra Mohan Bhuma, and Srinivas Kumar Samayamantula, "The Analysis of Digital Mammograms Using HOG and GLCM Features," in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bangalore, India, 2018.

Xingyu Li and Konstantinos N. Plataniotis, "Color texture representation using circular-processing based Hue-LBP for histo-pathology image analysis," in 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 2016.

Yibo Li and Mingjun Liu, "Aerial Image Classification Using Color Coherence Vectors and Rotation & Uniform Invariant LBP Descriptors," in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2018.

Yan Shang, Weimin Hou, Ruihong Wu, and Zhiyong Meng, "Antinoise Rotation Invariant Texture Classification Based on LBP Features of Dominant Curvelet Subbands," in 2008 Second International Symposium on Intelligent Information Technology Application, Shanghai, China, 2008.

Zixi Xiang, Xueqiang Lv, and Kai Zhang, "An Image Classification Method Based on Multi-feature Fusion and Multi-kernel SVM," in 2014 Seventh International Symposium on Computational Intelligence and Design, Hangzhou, China, 2014.

Chandrashekhar S. Janadri, B. G. Sheeparamatti, and Vishwanath Kagawade, "Multiclass classification of kirlian images using svm technique," in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.

Arun Kumar, Alaknanda Ashok, and M. A. Ansari, "Brain Tumor Classification Using Hybrid Model Of PSO And SVM Classifier," in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida (UP), India, 2018.

Dongping TianXiaofei ZhaoZhongzhi Shi, "Support Vector Machine with Mixture of Kernels for Image Classification," in Intelligent Information Processing VI (IIP 2012). Gullin, China: Springer, 2012, pp. 68-76.

Dinda Aulia Gustian et al., "Classification of Troso Fabric Using SVM-RBF Multi-class Method with GLCM and PCA Feature Extraction," in Internasional Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, 2019, pp. 7-11.

Miftahus Sholihin, Siti Mujilahwati, and Retno Wardhani, "CLASSIFICATION OF BATIK LAMONClassification of Batik Lamongan Based on Features of Color, Texture and Shape," Jurnal Ilmiah Kursor, vol. 9, no. 1, pp. 25-32, Juli 2017.

Raynaldi Fatih Amanullah, Ade Pujianto, Bayu Trisna Pratama, and Kusrini Kusrini, "DETEKSI MOTIF BATIK MENGGUNAKAN Deteksi Motif Batik Menggunakan Ekstraksi Tekstur dan Jaringan Syaraf Tiruan," in Seminar Nasional Teknologi Informasi dan Multimedia, Yogyakarta, 2018, pp. 31-36.

Published
2021-02-13
How to Cite
Andono, P. N., & Rachmawanto, E. H. (2021). Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 1 - 9. https://doi.org/10.29207/resti.v5i1.2615
Section
Artikel Teknologi Informasi