SISTEM PENGENALAN KARAKTER DENGAN JARINGAN SYARAF TIRUAN ALGORITMA PERCEPTRON

Authors

DOI:

https://doi.org/10.30872/jim.v7i3.94

Abstract

Jaringan syaraf tiruan (JST) adalah model pembelajaran yang dapat digunakan untuk pengenalan objek. Perceptron merupakan salah satu algoritma pembelajaran dari JST yang dapat digunakan untuk mengenali objek berupa karakter. JST terdiri dari dua proses utama, yaitu proses training (pembelajaran) dan proses testing (pengujian). Pada sistem ini proses training membutuhkan data masukkan berupa citra karakter, dimana setiap karakter memiliki ciri yang berbeda. Data ciri setiap karakter berupa matriks berukuran 10x10 dan akan menjadi data masukkan pada struktur JST. Keluaran sistem ini berupa data teks hasil pengenalan citra karakter. Sebagai uji coba sistem ini akan menghasilkan keluaran sebanyak sepuluh kategori karakter, yaitu karakter A – J.

Author Biography

  • Anindita Septiarini, Mulawarman University
    Computer Science

References

Fausett, L. 1994. “Fundamentals of Neural Networks: Architectures, Algorithms, and Applications”, Prentice-Hall, New Jersey.

Kusumadewi, S. 2003, Artificial Intelligence Teknik dan Aplikasny, Graha Ilmu Yogyakarta

Munir. R, 2004, Pengolahan Citra Digital dengan Pendekatan Algoritmik, Informatika Bandung.

Siang, J.J. 2005. Jaringan Syaraf Tiruan dan Pemrogramannya Dengan Matlab. Yogyakarta: Penerbit Andi.

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Published

05-06-2016

Issue

Vol. 7 No. 3 (2012): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer

Section

Articles

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