Klasifikasi Bahasa Isyarat Amerika menggunakan Convolutional Neural Network
DOI:
https://doi.org/10.26418/justin.v10i1.47184Keywords:
CNN, Deep Learning, ASLAbstract
Bahasa Isyarat adalah bahasa untuk orang - orang yang memiliki kesulitan mendengar maupun bicara. Tetapi bahasa isyarat bukanlah bahasa yang banyak digemari oleh masyarakat, sehingga orang yang memiliki disabilitas tersebut akan semakin kesulitan. Pada jurnal ini akan menjelaskan mengenai klasifikasi bahasa isyarat Amerika dengan menggunakan Convolutional Neural Network (CNN). Pada penelitian ini akan dilakukan beberapa penelitian menggunakan parameter berbeda seperti pada preprocessing, penelitian akan dilakukan dengan melihat parameter horizontal flip. Selanjutnya penelitian juga dilakukan dengan melihat epoch. Penelitian ini dilakukan untuk memantau akurasi dan akurasi validasi. Model yang dibuat pada penelitian ini nilai akurasi yang lebih tinggi saat memprediksi huruf v, dan n. Hasil nilai akurasi dari penelitian ini adalah 82.1%
Sign Language is a language for people who have hearing and speech difficulties. But sign language is not a language that is favored by many people, so people with disabilities will find it increasingly difficult. This journal will explain the classification of American sign language using the Convolutional Neural Network (CNN). In this study, several studies will be carried out using different parameters such as in preprocessing, research will be carried out by looking at the horizontal flip parameter. Furthermore, research was also carried out by looking at the epoch. This study was conducted to monitor the accuracy and accuracy of the validation. The model made in this study has a higher accuracy value when predicting the letters v, and n. The result of the accuracy value of this study is 82.1%
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