PREDIKSI HARGA EMAS MENGGUNAKAN ALGORITMA SEASONAL AUTO REGRESSIVE INTEGRATED MOVING AVERAGE
Abstract
This study aims to develop a gold price prediction model using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The model is designed to address fluctuations in gold prices influenced by seasonal factors by utilizing historical gold price data as the basis for forecasting. The data used has undergone selection and transformation stages within the Knowledge Discovery in Databases (KDD) framework. The model Evaluation was conducted using two main metrics, namely Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), to measure prediction accuracy and performance. The results indicate that the SARIMA model is capable of providing high-accuracy predictions, as evidenced by an RMSE of 17.393 and a MAPE of 0.65863, reflecting a relatively low prediction error. These findings affirm the reliability of SARIMA in handling time series data with seasonal patterns while contributing to the development of commodity price prediction methods, particularly for gold. The developed model is expected to serve as a reference for investors, market analysts, and other stakeholders in formulating more measured and data-driven investment strategies.
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References
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