Application of Neural Network in Letter Recognition Using the Perceptron Method
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Published:
Jun 1, 2022
Keywords:
Perceptron Method, Neural Network, Letter Recognition.
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Articles
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Abstract
Modeling with neural networks is the learning and adjustment of an object. The perceptron method is a learning method with supervision in a neural network system. In designing a neural network that needs to be considered is the number of specifications that will be identified. A neural network consists of a number of neurons and a number of inputs. To identify some letters, it takes several neurons to distinguish them. These neurons will generate a combination value that is used to identify the letters. so that the resulting network must have parameters that can be set by changing through the rules of learning with supervision.
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How to Cite
Suteja, B. (2022). Application of Neural Network in Letter Recognition Using the Perceptron Method. Instal : Jurnal Komputer, 14(01), 11–23. https://doi.org/10.54209/jurnalkomputer.v11i02.9
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[4] Y. Pangaribuan and M. Sagala, "Applying Artificial Neural Networks to Recognize Letter Patterns Using the Perceptron Method," Tech. information. Unique St. Thomas, vol. 02, no. 479, 2017.
[5] AH Hamdan Anik Nur, "APPLICATION OF PERCEPTRON METHOD ARTIFICIAL NEURAL NETWORK ON HIJAIYAH PATTERN RECOGNITION (Arabic LETTERS)," Pros. SAKTI (Seminar on Computing and Information Technology), no. Vol 2, No 1 (2017): Vol 2, No 1 (2017): Proceedings of the National Seminar on Computer Science and Information Technology, 2017.
[6] H. Surrisyad and AS Yazid, "Application of Artificial Neural Networks Using Learning Vector Quantization Methods in Recognizing Javanese Pegon Letter Patterns," JISKA (Journal of Inform. Sunan Kalijaga), vol. 5836, no. Kristanto, 2017.
[7] R. Muliono and J. Hakim Lubis, "Artificial Neural Network Recognition of Letter Patterns with the Habb Network," JTIK (Journal of Tech. Inform. Kaputama), vol. 2, no. 1, 2018.
[8] MZ Luhing and KM Suryaningrum, "Recognition of Russian Character Characters with the Perceptron Algorithm," Processor, vol. 13, no. 1, 2018.
[9] HA Sholeh, Y. Mulyani, and HD Saptama, "Comparative Study of Lampung Script Character Recognition with Roberts and Sobel Edge Detection Methods," Inov. developer. J. R&D, vol. 6, no. 03, 2018, doi:10.35450/jip.v6i03.94.
[10] Z. Azmi and H. Freizello, “Implementation of hopfields for hiragana character recognition,” J. Inf. syst. Computer Informatics., vol. 1, no. 1, 2017.
[11] M. Arifin, K. Asfani, and AN Handayani, "APPLICATION OF PERCEPTRON NEURAL NETWORKS IN RECOGNITION OF NOTATION PATTERNS," Symmetrical J. Tek. Mechanical, Electrical and Computer Science., vol. 9, no. 1, 2018, doi:10.24176/simet.v9i1.1737.
[12] M. Yanto, R. Sovia, and EPW Mandala, "PERCEPTRON ARTIFICIAL NEURAL NETWORK FOR PATTERN DETERMINATION OF AGRICULTURAL LAND IRRIGATION SYSTEMS IN COASTAL DISTRICT SELATAN WEST SUMATRA," Sebatik, vol. 22, no. 2, 2018, doi:10.46984/sebatik.v22i2.317.
[13] Y. Yulisman, "ANNAL NETWORK APPLICATION WITH PERCEPTRON METHOD TO DETERMINE THE TYPE OF E-LEARNING (CASE STUDY IN STMIK HANG TUAH PEKANBARU)," J. Computer Science., vol. 6, no. 2, 2018, doi:10.33060/jik/2017/vol6.iss2.58.
[14] MS Wibawa and IMD Maysanjaya, "MULTI LAYER PERCEPTRON AND PRINCIPAL COMPONENT ANALYSIS FOR BREAST CANCER DIAGNOSIS," J. Nas. Educator. Tech. information., vol. 7, no. 1, 2018, doi:10.23887/janapati.v7i1.12909.
[15] NO Rahardiani, WF Mahmudy, and Indriati, "Optimization of Multi-Layer Perceptron Weights Using Genetic Algorithms for Classification of Stroke Risk Levels.pdf," J. Pemb. Technol. inf. and Computer Science. Univ. Brawijaya, vol. 2, no. 8, 2018.
[16] AY Prathama, “ANN (ARTIFICIAL NEURAL NETWORK) APPROACH TO DETERMINING WORK WEIGHT PROCENTAGE AND ESTIMATION OF WORK STRUCTURE VALUE AT PRATAMA HOSPITAL, J. Teknoscience, vol. 7, no. 1, 2018, doi:10.22146/teknosains.30139.
[17] M. Wildan, P. Aldi, and A. Aditsania, “Analysis and Implementation of Long Short Term Memory Neural Network for Bitcoin Price Prediction,” e-Proceeding Eng., vol. 5, no. 2, 2018.
[18] H. Herman, L. Syafie, and D. Indra, "Recognition of Handwritten Numbers Using Artificial Neural Networks," Ilk. J. Ilm., vol. 10, no. 2, 2018, doi:10.33096/ilkom.v10i2.317.201-206.
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