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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Restu Ilahi, Muhammad
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Computer Science & IT

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The Digital Footprint of Public Attention: Forecasting Indonesian Gold Prices using Google Trends Index and Optimized Support Vector Regression Restu Ilahi, Muhammad; Wahyu Wijayanto, Arie
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.730

Abstract

To provide actionable forecasting insights for gold prices in Indonesia’s public sentiment-driven market, this study developed a machine learning framework using the Google Trends Index (GTI) as a sentiment proxy. We employed an Optuna-optimized Support Vector Regression (SVR) model to comparatively evaluate three feature sets (GTI, historical Lag, and a Mix) across seven forecasting horizons (t+1 to t+30). A key advantage of our approach was the identification of horizon-dependent predictor dynamics: results revealed that while historical data excelled for short-term forecasts (MAPE 0.50% at t+5), the contribution of GTI became vital for long-term accuracy, where the hybrid model achieved its peak performance (MAPE 1.92% at t+30). Notably, the GTI-only model showed solid standalone potential (MAPE < 20%). We conclude that a hybrid approach is most effective, validating GTI as a relevant predictor for Indonesia. Furthermore, the proposed SVR-Optuna framework offers a generalizable methodology for forecasting other sentiment-driven assets, providing a clear, actionable guide for model selection based on forecasting horizons.
Co-Authors Arie Wahyu Wijayanto