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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Sumarni, Cucu
Politeknik Statistika STIS, Jakarta, 13330, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Hybrid GSTAR-Machine Learning Model for Forecasting Tourists Numbers in Yogyakarta Sohibien, Gama Putra; Azmi, Annisa Nurul; Sofa, Wahyuni Andriana; Sumarni, Cucu; Prasetyo, Rindang Bangun; Putri, Christiana Anggraeni
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26381

Abstract

Tourism management in DI Yogyakarta is vital to ensure tourism benefits local communities. A key challenge lies in the uncertainty and spatial interdependence of tourist visits among neighboring regions. While the GSTAR model captures spatial relationships, its accuracy decreases with outliers, non-linearity, and assumption violations. To overcome these issues, this study integrates GSTAR with machine learning. Using 168 observations of tourist visits across DI Yogyakarta’s regencies/cities (January 2010–December 2023), GSTAR-GLS-XGBoost model achieved 22–34% lower RMSE than other models. Tourist numbers fluctuate greatly, with peaks in May, June, July, and December. Practically, these findings can help local governments and stakeholders optimize resource allocation, plan promotions, and prepare facilities during peak seasons for sustainable tourism management in DI Yogyakarta. 
Co-Authors Azmi, Annisa Nurul Prasetyo, Rindang Bangun Putri, Christiana Anggraeni Sofa, Wahyuni Andriana Sohibien, Gama Putra