The relationship between data skewness and accuracy of Artificial Neural Network predictive model

A. Larasati; A.M. Hajji; Anik Dwiastuti

doi:10.1088/1757-899X/523/1/012070

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The relationship between data skewness and accuracy of Artificial Neural Network predictive model

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A. Larasati, A.M. Hajji, Anik Dwiastuti

2019 IOP Conference Series: Materials Science and Engineering Vol. 523 Issue 1 Conference paper Cited by 16 SDG 9

Abstract

The purpose of this study is to investigate the relationship between data skewness in the output variable and the accuracy of artificial neural network predictive model. The artificial neural network predictive model is built using multilayer perceptron and consist of one output variable and six input variable, and the algorithm used is back propagation. Data used in this study is generated by conducting the simulations in 1000 cycles. Three categories of skewness used in the output variables are positive skewness, neutral, and negative skewness. The results show that data skewness does not have a significant effect on the accuracy of the artificial neural network predictive model. These results imply that artificial neural network predictive model has a higher capability to cope with skewed data due to its complexity in the hidden layer. © Published under licence by IOP Publishing Ltd.

Affiliations

Department of Industrial Engineering, Universitas Negeri Malang, Indonesia; Department of Civil Engineering, Universitas Negeri Malang, Indonesia

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