<![CDATA[Sales of electronic products are highly influenced by various internal and external factors, which require accurate prediction models to support strategic decision making. This study aims to evaluate the performance of ARIMA models in projecting future sales with a case study of electronic products, using monthly sales data collected from company reports and industry databases. The methods used include checking the stationarity of the data using the Augmented Dickey-Fuller (ADF) test, applying differentiation if necessary, and selecting ARIMA parameters based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis. The results of the analysis show that ARIMA models successfully capture seasonal and trend patterns, with performance evaluated using accuracy metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The implications of this study suggest the importance of considering external factors in modeling to improve prediction accuracy, as well as exploring other modeling approaches that can be more responsive to changing market dynamics.]]>
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