Optical Character Recognition Menggunakan Jaringan Syaraf Tiruan dengan Algoritma Perseptron


  • Gusni Sari Rambe * Mail Universitas Budi Darma, Medan, Indonesia
  • (*) Corresponding Author
Keywords: Optical Character Recognition; Artificial Neural Network; Perceptron Algorithm; Digital Image

Abstract

The development of technology today has greatly influenced the development of science, one of which is in the recognition of letter, number and character patterns (pattern recognition). The problem that arises is that each character in the computer or the result of a pattern scan entered into the computer must have a different character pattern recognition, this results in limited human ability to clarify or describe a pattern based on quantitative measurements of the main features or properties of a pattern of letters, numbers and characters. To overcome existing problems, Artificial Neural Networks (ANN) are a tool for solving problems, especially in areas involving grouping and pattern recognition, in general, Artificial Neural Networks have a system that is able to think, consider the actions to be taken, and make decisions like humans. Through the process of character pattern recognition using the Perceptron algorithm, it is hoped that in the future this character recognition system can have a major impact on character recognition that is not in the computer and can make it easier for users to recognize characters and it is also hoped that this algorithm can be used for other objects that will be pattern recognition.

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References

M. Z. Luhing and K. M. Suryaningrum, “Pengenalan Karakter Huruf Rusia dengan Algoritma Perceptron,” Processor, vol. 13, no. 1, pp. 1160–1172, 2018.

R. Sovia and M. Yanto, “Jaringan Syaraf Tiruan Analisa Pengaruh Gizi Buruk Terhadap Perkembangan Balita dengan Algoritma Perceptron,” J. Ilm. Media SISFO, vol. 12, no. 1, pp. 1003–1011, 2019.

M. Yanto, “Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan Sidang Skripsi,” J. Teknoif, vol. 5, no. 2, pp. 79–87, 2017, doi: 10.21063/jtif.2017.v5.2.79-87.

A. C. Oktavianti et al., “Pengenalan Pola Karakter Aksara Jawa Menggunakan Metode Perceptron Aplikasi Carakan,” pp. 159–164, 2021.

D. A. Ulandari, D. Swanjaya, T. Informatika, F. Teknik, U. Nusantara, and P. Kediri, “Perbandingan Transformasi Data pada Penentuan Peserta Bimbingan Belajar Menggunakan Metode Perceptron,” pp. 191–196, 2020.

R. Candra and N. Santi, “Teknik Perbaikan Kualitas Citra Satelit Cuaca dengan Sataid,” J. Teknol. Inf. Din., vol. 16, no. 2, pp. 101–109, 2011.

M. Cheriet, N. Kharma, C.-L. Liu, and C. Y. Suen, Character Recognition Systems A Guide for Students and Practioners. New Jersey: John Wiley & Sons, Inc, 2007.

F. Rahma, Pengolahan Citra Digital Deteksi Tepi. 2020.

E. F. Yuwitaning, N. Andini, F. T. Elektro, and U. Telkom, “IMPLEMENTASI METODE HIDDEN MARKOV MODEL UNTUK DETEKSI TULISAN TANGAN Implementation of Hidden Markov Model Method for Handwriting Detection,” vol. 1, no. 1, pp. 396–402, 2014.

M. Wahyudi, L. M. Gultom, and Solikhun, Implementasi Komputasi Quantum Pada Jaringan Saraf Tiruan. Yogyakarta: Yayasan Kita Menulis, 2020.

P. Sulistyorini, “Pemodelan Visual dengan Menggunakan UML dan Rational Rose,” vol. XIV, no. 1, pp. 23–29, 2009.

R.A.S and M. Shalahuddin, Rekayasa Perangkat Lunak. Bandung, 2016.

R. Yesputra and S. Utara, Belajar Visual Basic . Net dengan Visual Studio 2010, no. December. 2017.


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