Andriana, Budi Mulyanti, Isma Widiaty, Zulkarnain, Ike Yuni Wulandari
This purpose study is to assist the social interaction of deaf and dumb inclusion students with normal students and teachers in vocational education using a converter equipped with a Time of Flight (ToF) and infrared camera sensor and an extreme learning machine (ELM) algorithm. The conversion method uses the following processes: (i) Pre-Processing, (ii) 3D and 2D Segmentation, (iii) 3D and 2D Extraction, (iv) Hand Gesture Recognition Data Processing, (v) Pattern Recognition, and (vi) Pattern to Text and Voice Convert. The Indonesian sign language for social interaction tested has 11 words. The experimental demonstration was carried out with the help of an artificial network ELM algorithm that uses a hidden layer of feedforward Neural networks. The ELM parameters consist of: (i) hand position data in x, y, z, (2) data rotation, (iii) finger detection in x, y, z, (iv) extended finger position, and (v) non-extended finger position. Test results in the form of ELM parameters of 11 words in the form of data showing that the conversion of each language used into text and sound that is tested by ELM with the time of flight and infrared cameras has a success rate where students can socially interact with normal students and teachers in public schools vocational. © School of Engineering, Taylor's University
Departemen Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi no 299, Bandung, 40154, Indonesia; Departemen Teknik Elektro, Universitas Langlangbuana, Jl. Karapitan No 116, Bandung, 40261, Indonesia; Departemen Teknik Elektro, Universitas Nurtanio, Jl. Padjajaran No 219, Bandung, 40174, Indonesia
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