Journal of Engineering and Applied Sciences

Year: 2017
Volume: 12
Issue: 22
Page No. 5889 - 5894

Tensor Decomposition and Algorithm a Genetic-Learning Vector Quantification in Golek Menak Dance Motion

Authors : Joko Sutopo, Adhi Susanto, Insap Santosa and Teguh Barata Adji

Abstract: Golek Menak dance is a transformational form of Golek Menak puppet show. This dance has a lot of different motion attitudes and has meaning of each motion physically and mathematically (aspect of tensor, flexibility, geometry). Considering that, now there are still many people who have not understood types of dance motions both classical and traditional dances and meaning of each dance motion so that this study presents introduction of motion attitude types in Golek Menak dance. This study used Kinect sensor to obtain skeleton data of dancer. The introduction of motion attitude types in the Golek Menak dance through 4 stages, namely data collection, feature extract with tensor decomposition, classification of motion attitude introduction using Learning Vector Quantification (LVQ) method with Genetic Algorithm (AG) optimization. This study aims at helping people recognize motion of Golek Menak dance. Modernity of this study is combination of tensor decomposition, LVQ and AG to identify motion attitude types of Golek Menak dance. This study took samples of jogetan and sabetan motions as a part of Golek Menak dance motion. Based on the results of study, test for suitability of motion attitude recognition (Jogetan and Sabetan) found percentage of 90%.

How to cite this article:

Joko Sutopo, Adhi Susanto, Insap Santosa and Teguh Barata Adji, 2017. Tensor Decomposition and Algorithm a Genetic-Learning Vector Quantification in Golek Menak Dance Motion. Journal of Engineering and Applied Sciences, 12: 5889-5894.

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