Comparison of Artificial Neural Network and Multiple Regression on Favoured Halal Destination - KasturyGohain, Miney A/P K.N. Soman, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Relationship between assumed variables has been redundantly carried out by multiple regression analysis and correlation analysis. The application of unconventional ways to learn deep into human minds to gauge the behavior and intentions will increase the weight of reasonably accurate prediction. Thus this study aims to empirically verify the prediction with the support of artificial neural network and multiple regressions. The quality of the neural network is often collated in terms of estimated error. By distinction non-linear and non-parametric procedures are not simple to implement unlike artificial neural network’s applicability without manipulative assumptions. Results show that the coefficient of determination driven from multiple regression analysis is able to explain dependent variables with the support of the input variables. Despite this the error for artificial neural network is lower compared to multiple regression analysis. Thus, the predictive performance through artificial neural network is considered to be stronger approach compared to multiple regression analysis. As global tourism industry is ever more dynamic business, recognizing the needs, desires, demands and behaviors of international travelers plays a vital part in the growth of destinations. Therefore, the primary objective of this investigation is to predict the outcomes of halal destination by comparing multiple regression and artificial neural network. Outcome reflects that artificial neural network prediction is firmer compared to multiple regression analysis.