Sign Language A-Z Alphabet Introduction American Sign Language using Support Vector Machine

Muhammad Rasuandi, Muhammad Fachrurrozi, Anggina primanita

Abstract

Deafness is a condition where a person's hearing cannot function
normally. As a result, these conditions affect ongoing interactions,
making it difficult to understand and convey information.
Communication problems for the deaf are handled through the
introduction of various forms of sign language, one of which is
American Sign Language. Computer Vision-based sign language
recognition often takes a long time to develop, is less accurate, and
cannot be done directly or in real-time. As a result, a solution is
needed to overcome this problem. In the system training process,
using the Support Vector Machine method to classify data and testing
is carried out using the RBF kernel function with C parameters,
namely 10, 50, and 100. The results show that the Support Vector
Machine method with a C parameter value of 100 has better
performance. This is evidenced by the increased accuracy of the RBF
C=100 kernel, which is 99%.

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References

G. R. Mauludi and A. Setiyadi, “Pembangunan Alat Penerjemah Huruf dan Angka Bahasa

Indonesia bagi Tunarungu dan Tunawicara menggunakan Arduino,” 2019.

I. Nadia, “Jurnal Mahasiswa Universitas Muhammadiyah Ponorogo,” Jurnal Mahasiswa

Universitas Muhammadiyah Ponorogo, 2018.

M. E. Al Rivan, H. Irsyad, K. Kevin, and A. T. Narta, “Pengenalan Alfabet American Sign

Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented

Gradients,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 5, no. 3, Jan. 2020, doi:

28932/jutisi.v5i3.1936.

A. Rozani, “PENERAPAN METODE JARINGAN SYARAF TIRUAN PADA APLIKASI

PENGENALAN BAHASA ISYARAT ABJAD JARI,” 2017.

P. A. Octaviani, Y. Wilandari, and D. Ispriyanti, “PENERAPAN METODE KLASIFIKASI

SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR

(SD) DI KABUPATEN MAGELANG,” vol. 3, no. 4, pp. 811–820, 2014, [Online]. Available:

http://ejournal-s1.undip.ac.id/index.php/gaussian

T. M. Kadarina et al, “Pengenalan Bahasa Pemrograman Python menggunakan Aplikasi Games

untuk Siswa/i di Wilayah Kembangan Utara,” 2019. [Online]. Available:

https://codecombat.com/.

I. Monika Parapat and M. Tanzil Furqon, “Penerapan Metode Support Vector Machine (SVM)

Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak,” 2018. [Online]. Available: http://jptiik.ub.ac.id

A. R. Shetty, F. B. Ahmed, and V. M. Naik, “CKD Prediction Using Data Mining Technique

As SVM And KNN With Pycharm,” International Research Journal of Engineering and

Technology, p. 4399, 2008, [Online]. Available: www.irjet.net

S. Wulan Purnami and W. wibowo, “PARAMETER OPTIMIZATION OF SUPPORT

VECTOR MACHINE USING TAGUCHI APPROACH FOR HIGH-DIMENSIONAL

DATA,” 2017.

R. I. Borman, B. Priopradono, and A. R. Syah, Klasifikasi Objek Kode Tangan pada

Pengenalan Isyarat Alphabet Bahasa Isyarat Indonesia (Bisindo). 2017.

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