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Submitted
14 April 2021
Accepted
31 May 2021
Published
1 June 2021
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Abstract

Indonesian Sign Language or BISINDO is a two-handed sign language that is used as a liaison in communication. BISINDO is used by people who have limited speech or hearing, but not for other communities. This causes BISINDO users have difficulties in conveying information because only a few people understand BISINDO. Therefore, an application was developed to help communication between BISINDO users and Indonesian in realtime. BISINDO classification is carried out using the Convolutional Neural Network method and the MobilenetV2 architecture using tensorflow. The classification results are used as a model for android which is then used as a sound. Based on model testing, the resulting accuracy rate resulted in 54.8% in the classification of 30 specified languages. Thus, the performance of the model can be said to be not optimal in classifying. Based on the application testers to 30% of respondents, it was found that respondents strongly agreed with this application with an average value of 83.95%.

Keywords

BISINDO Tensorflow Convolutional Neural Network MobilenetV2 Android BISINDO Tensorflow Convolutional Neural Network MobilenetV2 Android

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite
Nasha Hikmatia A.E., & Zul, M. I. (2021). Aplikasi Penerjemah Bahasa Isyarat Indonesia menjadi Suara berbasis Android menggunakan Tensorflow. Jurnal Komputer Terapan, 7(1), 74–83. https://doi.org/10.35143/jkt.v7i1.4629
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References

  1. R. A. Mursita, "Respon Tunatungu Terhadap Penggunaan Sistem Bahasa Isyarat Indonesia (SIBI) dan Bahasa Isyarat Indonesia (BISINDO)," INKLUSI Journal of Disability Studies, vol. 2, pp. 221-232, 2015.
  2. T. Handika, R. I. M. Zen, M. D. P. Lestari and I. Sari, "Gesture Recognition For Indonesian Sign Language (BISINDO)," Journal of Physics: Conference Series, vol. 1028, no. 1, pp. 1-8, 2018.
  3. M. Luter, K. Frehadtomo, P. R. N. Dayawati and F. N. Prawita, "Sign To Voice Aplikasi Alat Bantu Komunikasi Untuk Tuna Rungu Wicara," 2015.
  4. M. I. Zul, "Feature Extraction For Hand Shape Recognition By Using IP Camera," In Regional Conference on Computer and Information Engineering, 2018.
  5. P. Yugopuspito, I. M. Murwantara and J. Sean, "Mobile Sign Language Recognition for Bahasa Indonesia Using Convolutional Neural Network," Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia, pp. 84-91, 2018.
  6. G. Gumelar, H. Hafiar and P. Subekti, "Bahasa isyarat indonesia sebagai budaya tuli melalui pemaknaan anggota gerakan untuk kesejahteraan tuna rungu," INFORMASI : Kajian Ilmu Komunikasi, vol. 48, no. 1, pp. 65-78, 2018.
  7. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. C. Chen, "Mobilenetv2: Inverted Residuals and Linear Bottlenecks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
  8. "TensorFlow," [Online]. Available: tensorflow.org. [Accessed 28 September 2020].
  9. I. GoodFellow, Y. Bengio and A. Courville, Deep Learning, MIT press, 2018.