Performance of Linear Programming Asymmetric Parameter Fuzzy Modelling Based on Statistical Error Measurement

Nor Kamariah  Kamaruddin; Muhammad Ammar  Shafi; Gusman  Nawanir; Nur Azia Hazida  Mohamad Azmi; Aliya Syaffa  Zakaria; Zulfana  Lidinillah

doi:10.37934/ard.130.1.126133

Authors

Nor Kamariah Kamaruddin Department of Technology and Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia
Muhammad Ammar Shafi Department of Technology and Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia
Gusman Nawanir Faculty of Economics and Business, Universitas Islam Riau, Jl. Kaharudin Nasution, No. 113, Kota Pekanbaru, Riau, Indonesia
Nur Azia Hazida Mohamad Azmi Department of Technology and Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia
Aliya Syaffa Zakaria Department of Technology and Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia
Zulfana Lidinillah Department of Technology and Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia

DOI:

https://doi.org/10.37934/ard.130.1.126133

Keywords:

Fuzzy modeling, comparison models, measurement error, performance of model

Abstract

Modelling the relationship between a scalar answer and one or more explanatory factors using a linear technique is known as linear regression. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory’s concept can deal with data not to a precise point value (uncertainty data), this study applied the fuzzy linear regression with asymmetric parameter (FLRWAP) to 1000 row of simulation data. Five independent variables with different combination of variable types were considered. Other than that, the performance of the models such as the parameter, error and explanation for the model were included using two measurement statistical errors which is mean square error and root mean square error. FLRWAP found the results of least value of mean square error (MSE) and root mean square error (RMSE) is less than another model with 107.88 and 10.39 respectively.