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Penggunaan Convolutional Neural Network Sebagai Pengenalan Huruf Bahasa Ibrani

Julian Rusli Tee Baldi(1*),Yohannes Yohannes(2),Siska Devella(3)
(1) Universitas Multi Data Palembang
(2) Universitas Multi Data Palembang
(3) Universitas Multi Data Palembang
(*) Corresponding Author
DOI : 10.35889/jutisi.v12i1.1090

Abstract

Hebrew is important language due to it has a great attachment with Edenics. Edenics is ancestor language of Semitic that broke down within 70 languages for about 3.784 years ago that influenced many languages in the world also have connection to Hebrew. Hebrew has an important role because it’s used to study Bible and Mishnah. Research was made as a translate system for Hebrew Letter and the author used 27 of Hebrew letters, using Convolutional Neural Network method with AlexNet architecture. The Hebrew letter recognition made by using the Python.The dataset that used seperated into 27 letters for each of every training and testing data. The amount of training data is 3.638 pictures and testing data is 810 pictures. The highest accuration from 3 optimizers were obtained from Adam optimizer with 81,5% accurate.

Key Words: AlexNet Architecture; Hebrew, Convolutional Neural Network

 

Abstrak

Bahasa Ibrani penting dikarenakan erat hubungannya dengan Edenics. Edenics ialah bahasa Ibu.Semit yang tersebar ke 70 bahasa kurang lebih 3.784 tahun yang lalu dan berpengaruh besar pada banyak bahasa di bumi serta memiliki keterkaitan dengan bahasa Ibrani. Bahasa Ibrani berperan penting dikarenakan digunakan untuk mempelajari Alkitab dan Mishnah. Penelitian dibuat sebagai sistem penerjemah huruf bahasa Ibrani, dan penulis menggunakan 27 huruf alfabet Ibrani serta menggunakan metode Convolution Neural Network dengan arsitektur AlexNet. Pengenalan huruf Ibrani dibuat menggunakan Python. Dataset yang digunakan terbagi menjadi 27 huruf pada setiap data latih dan uji. Total data latih ialah 3.638 gambar. Total data uji ialah 810 gambar. Penggunaan optimizer seperti Adam, SGD dan RMSprop menghasilkan nilai precision, recall, dan accuracy yang berbeda. Hasil akurasi tertinggi diperoleh dari optimizer Adam dengan tingkat akurasi sebesar 81,5%.

Keywords


Arsitektur AlexNet; Bahasa Ibrani; Convolutional Neural Network

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