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All Journal Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF Makara Journal of Technology JIEET (Journal of Information Engineering and Educational Technology) International Journal of Technology And Business JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Makara Journal of Technology
Endang Supriyati
Universitas Muria Kudus
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Makara Journal of Technology

 1 
Recognition System of Indonesia Sign Language based on Sensor and Artificial Neural Network Supriyati, Endang; Iqbal, Mohammad
Makara Journal of Technology Vol 17, No 1 (2013)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.822 KB) | DOI: 10.7454/mst.v17i1.258

Abstract

Sign language as a kind of gestures is one of the most natural ways of communication for most people in deaf community. The aim of the sign language recognition is to provide a translation for sign gestures into meaningful text or speech so that communication between deaf and hearing society can easily be made. In this research, the Indonesian sign language recognition system based on flex sensors and an accelerometer is developed. This recognition system uses a sensory glove to capture data. The sensor data that are processed into feature vector are the 5-fingers bending andthe palm acceleration when performing the sign language. The most important part of the recognition system is a featureextraction. In this research, histogram is used as feature extraction. The extracted features are used as data training and data testing for Adaptive Neighborhood based Modified Backpropagation (ANMBP). The system is implemented andtested using a data set of 1000 samples of 50 Indonesia sign, 20 samples for each sign. Among these 500 data were usedas the training data, and the remaining 500 data were used as the testing data. The system obtains the recognition rate of91.60% in offline mode.
Co-Authors Aditya, Dio Rizki Ahmad Abdul Chamid, Ahmad Abdul Handini Arga Damar Rani Idni Irsalina Khotimah,, Tutik Maulana, Muhammmad Rieza Maulana, Muhammmad Rieza Mohammad Iqbal Nindyasari, R. Nurmawati, Puji Pramitha, Agnes Rizkysari Meimaharani Sholihul Ibad Sholikhah, Maulida Ni’matus Sholikhah, Maulida Ni’matus Tri Listyorini Tutik Khotimah Uswatun Hasanah Wijaya, Deni Kusuma Wijaya, Deni Kusuma Yusuf, Afif Nor