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The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition
Abstract
Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.
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I. Prihandi, I. Ranggadara, S. Dwiasnati, Y. S. Sari, and Suhendra, “Implementation of Backpropagation Method for Identified Javanese Scripts,” in Journal of Physics: Conference Series, Institute of Physics Publishing, 2020. doi: 10.1088/1742-6596/1477/3/032020.
I. Ferdiansyah Katili, F. Dyah Esabella, and A. Luthfiarta, “Pattern Recognition Of Javanese Letter Using Template Matching Correlation Method,” Journal of Applied Intelligent System, vol. 3, no. 2, pp. 49–56, 2018.
A. Susanto, C. Atika Sari, I. U. W. Mulyono, and M. Doheir, “Histogram of Gradient in K-Nearest Neighbor for Javanese Alphabet Classification,” Scientific Journal of Informatics, vol. 8, no. 2, pp. 289–296, Nov. 2021, doi: 10.15294/sji.v8i2.30788.
G. A. Robby, A. Tandra, I. Susanto, J. Harefa, and A. Chowanda, “Implementation of Optical Character Recognition using Tesseract with the Javanese Script Target in Android Application,” Procedia Comput Sci, vol. 157, pp. 499–505, 2019, doi: 10.1016/j.procs.2019.09.006.
A. Susanto, D. Sinaga, C. A. Sari, E. H. Rachmawanto, D. Rosal, and I. M. Setiadi, “A High Performace of Local Binary Pattern on Classify Javanese Character Classification,” Scientific Journal of Informatics, vol. 5, no. 1, pp. 2407–7658, 2018, [Online]. Available: http://journal.unnes.ac.id/nju/index.php/sji
L. D. Krisnawati and A. W. Mahastama, “Building classifier models for on-off javanese character recognition,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2019. doi: 10.1145/3366030.3366050.
N. Irsalinda, S. Surono, I. Dwi, and R. Sary, “Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction,” 2022. [Online]. Available: https://doi.org/11.26554/sti.2222.7.3.269-274
M. Daniel, J. Raharjo, and K. Usman, “Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine,” Engineering Journal, vol. 24, no. 5, pp. 135–144, Sep. 2020, doi: 10.4186/ej.2020.24.5.135.
D. U. K. Putri, D. N. Pratomo, and Azhari, “Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 21, no. 2, pp. 346–353, Apr. 2023, doi: 10.12928/TELKOMNIKA.v21i2.24266.
F. Damayanti, Y. K. Suprapto, and E. M. Yuniarno, “Segmentation of Javanese Character in Ancient Manuscript using Connected Component Labeling,” in CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 412–417. doi: 10.1109/CENIM51130.2020.9297954.
S. Ahlawat, A. Choudhary, A. Nayyar, S. Singh, and B. Yoon, “Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN).,” Sensors (Basel), vol. 20, no. 12, pp. 1–18, Jun. 2020, doi: 10.3390/s20123344.
A. K. Nugroho, I. Permadi, and M. Faturrahim, “Improvement Of Image Quality Using Convolutional Neural Networks Method,” Scientific Journal of Informatics, vol. 9, no. 1, pp. 95–103, May 2022, doi: 10.15294/sji.v9i1.30892.
Hassan M. Najadat, Ahmad A. Alshboul, and Abdullah F. Alabed, “Arabic Handwritten Characters Recognition usingConvolutional Neural Network,” in 10th International Conference on Information and Communication Systems (ICICS, 2019, pp. 147–151.
I. Khandokar, M. Hasan, F. Ernawan, S. Islam, and M. N. Kabir, “Handwritten character recognition using convolutional neural network,” J Phys Conf Ser, vol. 1918, no. 4, p. 042152, Jun. 2021, doi: 10.1088/1742-6596/1918/4/042152.
T. Q. Vinh, L. H. Duy, and N. T. Nhan, “Vietnamese handwritten character recognition using convolutional neural network,” IAES International Journal of Artificial Intelligence, vol. 9, no. 2, pp. 276–283, Jun. 2020, doi: 10.11591/ijai.v9.i2.pp276-283.
R. Parthiban, R. Ezhilarasi, and D. Saravanan, “Optical Character Recognition for English Handwritten Text Using Recurrent Neural Network,” in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE, Jul. 2020, pp. 1–5. doi: 10.1109/ICSCAN49426.2020.9262379.
N. B. Muppalaneni, “Handwritten Telugu Compound Character Prediction using Convolutional Neural Network,” in International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020, pp. 1–4. doi: 10.21227/phg6-m127.
A. Susanto, C. Atika Sari, I. U. W. Mulyono, and M. Doheir, “Histogram of Gradient in K-Nearest Neighbor for Javanese Alphabet Classification,” Scientific Journal of Informatics, vol. 8, no. 2, pp. 289–296, Nov. 2021, doi: 10.15294/sji.v8i2.30788.
D. Sutaji and R. Dijaya, “Classification of Milk Fish Quality using Fuzzy K-Nearest Neighbor Method Based on Form Descriptor and Co-Occurrence Matrix,” J Phys Conf Ser, vol. 1179, no. 1, p. 012021, Jul. 2019, doi: 10.1088/1742-6596/1179/1/012021.
S. Winiarti, F. I. Indikawati, A. Oktaviana, and H. Yuliansyah, “Consumable Fish Classification Using k-Nearest Neighbor,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2020. doi: 10.1088/1757-899X/821/1/012039.
C. K. Dewa, A. L. Fadhilah, and A. Afiahayati, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 12, no. 1, p. 83, Jan. 2018, doi: 10.22146/ijccs.31144.
Y. Sugianela and N. Suciati, “Javanese Document Image Recognition Using Multiclass Support Vector Machine,” Communication & Information Technology) Journal, vol. 13, no. 1, pp. 25–30, 2019.
DOI: https://doi.org/10.33633/jais.v8i2.8450
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