Pemodelan dan Prediksi Harga Emas Menggunakan Metode ARIMA pada Data Time Series - Yesni Malau; Eni Pudjiarti; Fintri Indriyani; Riswandi Ishak; Asep Sayfulloh; Wahyutama Fitri Hidayat (Universitas Bina Sarana Informatika, 2026)
This study aims to analyze and predict gold price movements using a time series approach with the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this research are historical daily gold closing prices from 2020 to 2026 obtained from Investing.com, consisting of 1,568 data. The research stages include data collection, preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis, parameter estimation, diagnostic checking, and model accuracy evaluation. The results indicate that the data are stationary with a p-value < 0.05. Based on the identification and model selection process, the ARIMA (3,0,3) model was identified as the best model with an Akaike Information Criterion (AIC) value of 15449.326. Model evaluation results show an RMSE of 120.86, MAE of 95.02, and MAPE of 5.48%. The MAPE value below 10% indicates that the model has good accuracy in predicting gold prices. Therefore, the ARIMA model can be used as an effective approach to predict gold price movements based on historical data.