<![CDATA[High gold price volatility due to global economic instability poses challenges in investment decision-making. This research aims to develop a web-based gold price prediction application using a time series model, focusing on the Gated Recurrent Unit (GRU) algorithm. This application is designed to present real-time, accurate, and easily accessible gold price predictions, thereby increasing the efficiency and transparency of information for investment decision making. The development process starts from collecting and preprocessing daily gold price data for the period 2013-2023, then comparing four predictive models: LSTM, GRU, ARIMA, and XGBoost. Evaluation is performed using MAE, RMSE, and R² metrics. Results showed that GRU provided the best performance with an RMSE value of 17.76 and R² of 0.9410. The GRU model is integrated into a web application using the Flask framework, with an interactive HTML-based interface and Chart.js visualization. This application presents real-time gold price predictions and can be accessed by general users and investors. The results of this study show that the time series approach with GRU is effective in projecting gold prices, and can be a relevant tool in supporting data-based investment decisions.]]>
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