Handgesture To Text Dengan Metode Artificial Intelligence KNN (K-Nearest Neighbour)

I Putu Wijaya Merta, I Made Gede Sunarya, I Ketut Resika Arthana

Abstract


 Communication is the process of sending a message from one person to another. With  communication, the interaction will be more meaningful. With communication, a man can express his desire to communicate, express feelings, provide information, express
opinions, ideas and thoughts verbally (verbal) and non-verbal (gestures). Children with deaf disabilities using sign language to communicate with its environment. While the environment is generally a lot more people who understand spoken language rather than sign language so that children with hearing difficulties understand verbal expression of the environment and the environment is also difficult to understand the sign language used by deaf children. Because of the limitations of language communication and poverty experienced by persons with Disabilities Deaf Mute. So impaired speech with disabilities need to know sign language to facilitate communication both within the family and society environment. The technique used is to capture the position of the hand, to extract the shape of the hand, and then classify them. To locate the hand of each frame is generated, the author uses HaarClassifier previously undertaken training beforehand. Then used to extract the shape of the hand, skin detection and noise removal followed by thresholding and normalization. After the hand shape is obtained, the binary image is classified based on the shape of the hands of a collection of images of hand signals used as training data. Classification algorithms used by the author is the algorithm of K-Nearest neigbors. After a test of the system to perform system testing of 10 people by charging a 3 shape hand each corresponding letter SIBI. Alphabet letters that can be translated are alphabet letters a, b, c, d, e, f, g, h, i, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y. Alphabet letters j and z can not be translated because the shape of the hand movement. The final result obtained is 91.8% literacy.

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DOI: http://dx.doi.org/10.23887/karmapati.v4i1.19789

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