@article{MAKHILLJEAS2017122215065,
    title = {Tensor Decomposition and Algorithm a Genetic-Learning Vector
Quantification in Golek Menak Dance Motion},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {22},
    pages = {5889-5894},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5889.5894},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5889.5894},
    author = {Joko,Adhi,Insap and},
    keywords = {Decomposisi tensor,LVQ,AG,Tari Golek Menak,dancer,recognition},
    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%.}
    }