Minimum wage prediction based on K-Mean clustering using neural based optimized Minkowski Distance Weighting

  • Authors

    • Marselina Endah Hiswati
    • Achmad Fanany Onnilita Gaffar
    • Rihartanto .
    • Haviluddin .
    2018-03-05
    https://doi.org/10.14419/ijet.v7i2.2.12741
  • minimum wage, K-Mean clustering algorithm, MDW method, ANN-BP

  • Abstract

    Minimum Wage is a minimum standard used by employers to provide wages to workers in their business environment. The national minimum wage is the average of the provincial minimum wage. There are many factors need to be considered to set a minimum wage. The aim of this study is to predict the minimum wage based on K-Mean clustering concept. The Minkowski Distance Weighting (MDW) then used to estimate a value at the observation point in a cluster by using a linear combination of values of all cluster members around observation point mapped in 3-dimensional Cartesian coordinates. The prediction result by MDW then optimized by Artificial Neural Network Back Propagation (ANN-BP) to obtain a smaller Mean Absolute Percentage Error (MAPE). The net structure which already trained then used to predict the minimum wage for next year.

     

     

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  • How to Cite

    Endah Hiswati, M., Fanany Onnilita Gaffar, A., ., R., & ., H. (2018). Minimum wage prediction based on K-Mean clustering using neural based optimized Minkowski Distance Weighting. International Journal of Engineering & Technology, 7(2.2), 90-93. https://doi.org/10.14419/ijet.v7i2.2.12741

    Received date: 2018-05-12

    Accepted date: 2018-05-12

    Published date: 2018-03-05