PENGENALAN KARAKTER BRAILLE MEMANFAATKAN CONVOLUTIONAL NEURAL NETWORK
Abstract
ABSTRACT
Braille is a character designed for the blind. Braille letters consist of six raised dots arranged in three rows and two columns. Braille is read by touch, so finger sensitivity is very important. Braille combinations need to be memorized, making them very difficult to learn. This study discusses the introduction of braille characters using the Convolutional Neural Network (CNN) method. CNN will process 3 data sets, 60, 100, and 150 data with each data using 5, 10, 25, and 50 epochs. The highest accuracy value in the training process is 99.87% with a loss value of 0.232. In the recognition process, the highest accuracy is 99.62% with a recognition error of 1 image out of 260 images.
Keywords: Braille, CNN, Character, Recognition.
ABSTRAK
Huruf braille merupakan karakter yang dirancang untuk orang buta. Huruf braille terdiri dari enam titik timbul yang tersusun dalam tiga baris dan dua kolom. Huruf braille dibaca dengan menggunakan sentuhan, oleh sebab itu sensifitas jari sangat penting. Kombinasi huruf braille perlu dihafalkan, sehingga sangat sulit untuk dipelajari. Penelitian ini membahas pengenalan karakter braille dengan menggunakan metode Convolutional Neural Network (CNN). CNN akan memproses 3 kelompok data, 60, 100, dan 150 data dengan masing-masing data menggunakan 5, 10, 25, dan 50 epoch. Nilai akurasi tertinggi pada proses traning sebesar 99.87% dengan nilai loss sebesar 0.232. Dalam proses pengenalan akurasi tertinggi sebesar 99.62% dengan kesalahan pengenalan 1 gambar dari 260 gambar.
Kata Kunci: Braille, CNN, Karakter, Pengenalan.
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References
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