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This study aimed to model the number of foreign tourist visits in West Java. The number of a tourist visit is part of the discrete data so that the normal distribution approach become less precise in common modeling. In this study, the number of tourist visits was carried out using the Generalized Linear Model approach, combining the Poisson distribution and negative binomial distribution with the identity and log link function. The effect of internal covariates due to a surge in the number of tourists in a given month was also added to the modeling. tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models. The results showed that the four models obtained were equally good based on the mean absolute percent error (MAPE) values, while the model obtained with the negative binomial distribution integral probability log link function is the best model based on the Akaike information criterion (AIC), Bayesian information criterion (BIC) and integral probability transform (PIT) histogram values. The negative binomial with the log link function approach was then used to model and to predict the number of foreign visits. Plot using negative binomial and log link function, has a value closer to the actual data plot, also strong with the smallest AIC and BIC values.

Generalized Linear Model Approach for Time Series Count Data on Number of Foreign Tourists Modeling in West Java