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

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.