Prediction Tourist Visits With Multiple Linear Regressions in Artificial Neural Networks

Abstract

Tourist visit is a topic of discussion that has been much researched by previous researchers in conducting a prediction process. Many prediction models have been produced that refers to the use of several methods to obtain output in the form of information that is needed by the tour manager. Judging from the results of the study, it is still only focused on the discussion in producing output without testing the correlation of variables used as predictors. The problem in this study is how to predict the number of tourist visits by using Multiple Linear Regression (MLR) as a correlation test predictor variable and Artificial Neural Network (ANN) as a calculating machine in making predictions. The implementation of these two methods is very suitable to be used in terms of prediction, where the MLR method test results show the correlation of predictor variables used namely xxx correlation. Then in the prediction process that has been done to produce output with an accuracy value of xx%, the value of MSE xx% and RMSE is xx. Therefore, this research will be useful for managers of the tourism sector so that the goal achieved from this research is to assist the tourism office in seeing how many visits will occur in the next period.