KLASIFIKASI ALFABET BAHASA ISYARAT INDONESIA(BISINDO) DENGAN METODE TEMPLATE MATCHING DAN K-NEAREST NEIGHBORS (KNN)
In carrying out daily activities, humans must interact with each other by using language and communicating, but it is different from deaf people who communicate using sign language. To communicate with normal people who do not understand sign language requires an intermediary to translate sign langu...
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2020-06-17.
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| 100 | 1 | 0 | |a Andika Dicky Saputra, . |e author |
| 245 | 0 | 0 | |a KLASIFIKASI ALFABET BAHASA ISYARAT INDONESIA(BISINDO) DENGAN METODE TEMPLATE MATCHING DAN K-NEAREST NEIGHBORS (KNN) |
| 260 | |c 2020-06-17. | ||
| 500 | |a http://repository.upnvj.ac.id/6620/1/ABSTRAK.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/6620/10/LAMPIRAN.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/6620/11/ARTIKEL%20KI.pdf | ||
| 520 | |a In carrying out daily activities, humans must interact with each other by using language and communicating, but it is different from deaf people who communicate using sign language. To communicate with normal people who do not understand sign language requires an intermediary to translate sign language. This study aims to detect static and dynamic cue alphabets and classify them so that the output is a text that everyone can understand. This study uses a template matching method and the KNN algorithm. The data used is the result of video keyframe extraction consisting of 136 templates and 17 test images. The data then tested for compatibility using the template matching method and the final stage of classification using the KNN. In the compatibility test stage, the results were 85.04% for static sign alphabets, while dynamic sign alphabets were 84.65%. The KNN classification has an accuracy of 96.52%, so this study succeeded in classifying static and dynamic sign alphabets. | ||
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