PEMODELAN JUMLAH WISATAWAN DI JAWA TENGAH MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE - SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR) - Innosensia Adella; Dwi Ispriyanti; Hasbi Yasin (Jurnal Gaussian, 2022)
Space-time model is a model that can explain data with spatial and time characteristics. The Generalized Space Time Autoregressive (GSTAR) model is one of the generalized space-time models from the Space Time Autoregressive (STAR) model. The GSTAR model is more flexible when dealing with areas that have heterogeneous characteristics than the STAR model. The GSTAR model models time series data in multiple regions at once. This model can then be used to model data on the number of tourists in four regions in Central Java, namely Semarang, Jepara, Magelang and Semarang district for the 2014 to 2019 period. in Central Java. On the residual model, the Lagrange Multiplier Test is carried out and it is known that there is a correlation between the residuals. The modeling was continued by using the Generalized Space Time Autoregressive – Seemingly Unrelated Regression (GSTAR-SUR) model. GSTAR-SUR is one of the more efficient models used to model GSTAR with correlated residuals. Residual through the white-noise assumption test, it is found that the appropriate model is the GSTAR-SUR(2,1) model. This model can then be used in forecasting data on the number of tourists in Semarang, Jepara, Magelang and Semarang district in the next period