Forecasting Model for Tourist Numbers: A Case Study of Tamansari Banyuwangi Tourism Village

Developing a successful tourism village requires effective management, which includes forecasting tourist numbers. This study forecasts the number of visitors to Tamansari Banyuwangi Tourism Village, one of four tourist villages with independent status who had received national and international recognition. The Covid-19 pandemic from early 2020 to the end of 2022 had a considerable effect on tourist traffic around Tamansari Village’s popular destinations, including Kawah Ijen, Sendang Seruni, and Taman Gandung Terakota, as well as the management of Tamansari Tourism Village; specifically, BUMDesa Ijen Lestari. Time series forecasting was performed using the decomposition method and Holt-Winter exponential smoothing based on tourist data from 2016 to September 2023. Based on the smaller RMSE and MAPE values, the Holt- Winter forecasting model is better, with α = 0.05; β = 0.12; and γ = 0.35 as the utilized smoothing parameters. Additionally, the Holt-Winter method suggests an increasing trend for the following year’s forecasting results, with seasonal data being present in the July and December periods. Overall, it delivers more accurate information. The study’s findings can serve as a foundation for BUMDesa Ijen Lestari to create policies for the expansion of Tamansari Tourism Village and its surrounding destinations. Keywords: decomposition, Holt-Winter, Tamansari Banyuwangi tourism village, time series forecasting