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All Journal Inferensi Berdikari : Jurnal Ekonomi dan Statistik Indonesia
Muayyad, Muayyad
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Social Sciences

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Forecasting Tourist Arrivals in Bali: A Grid Search-Tuned Comparative Study of Random Forest, XGBoost, and a Hybrid RF-XGBoost Model Waciko, Kadek Jemmy; Susanti, Leni Anggraini; Muayyad, Muayyad; Fakhrurozi, Rifqi Nur
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23334

Abstract

Tourism planning, infrastructure growth, and economic stability. This study presents an extensive comparative evaluation of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and a novel Hybrid RF-XGBoost model in the prediction of monthly international tourist arrivals. A full time series dataset of a ten-year period (2014–2024) from the Central Bureau of Statistics of Bali was used for training and testing the models. Hyperparameter optimization using Grid Search with cross-validation (Grid Search CV) was used for all the machine learning models to obtain best predictive performance. Two robust metrics, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), were used to assess forecasting accuracy. Results show that the Random Forest model outperforms all competitors with lowest RMSE (41,772.68) and MAPE (6.30%), indicating high forecasting precision and robustness, especially during structural breaks such as the COVID-19 pandemic. The hybrid model also performs well, with LSTM indicating higher error rates, illustrating its shortcomings on small-to-medium-scale tourism time series. Besides, the study provides six-month ahead predictions (January–June 2025) with 95% prediction intervals, showing an ongoing trend of recovery. The findings affirm the superiority of bagging-based ensemble methods over polynomial-based methods in capturing nonlinearity, seasonality, and exogenous shocks in tourist demand. The study plugs the growing amount of data-driven tourism analytics by offering a reproducible, high-precision forecasting model for developing countries and seasonally driven destinations.
Improving Hierarchical Tourism Forecasting through the ARIMA-OC (ARIMA Based on The Optimal Combination) Method Waciko, Kadek Jemmy; Muayyad; Susanti, Leni Anggraini
Jurnal Ekonomi Dan Statistik Indonesia Vol. 5 No. 3 (2025): Berdikari: Jurnal Ekonomi dan Statistik Indonesia (JESI)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jesi.05.03.02

Abstract

The research explores the ARIMA-OC method (ARIMA based on the Optimal Combination approach) for Hierarchical Forecasting. In this approach, the ARIMA model is used to forecast each individual time series, and the Optimal Combination (OC) technique is applied to merge these initial forecasts into an updated set of predictions. The study compares the ARIMA model with the Exponential Tail Smoothing (ETS) model, with both models being integrated using five different strategies: the Bottom-up approach (BU), the Top-down approach using Forecasted Proportion (TDFP), two Top-down approaches based on Historical Proportions (TDHP1 and TDHP2), and the Optimal Combination approach (OC). To assess how ARIMA-OC performs with small samples, a simulation was carried out, revealing that ARIMA-OC surpasses the other methods according to the MASE metric. Furthermore, non-parametric tests like the Friedman test and the Nemenyi post-hoc test were used to validate the effectiveness of Hierarchical Forecasting.
Co-Authors Kadek Jemmy Waciko Leni Anggraini Susanti Rifqi Nur Fakhrurozi