Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia

Abstract

One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.