<![CDATA[This research aims to develop an accurate and reliable product sales prediction model using the Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) method. This approach is based on time series data analysis that takes into account seasonal patterns, trends, and external factors that can affect sales. Historical product sales data is analyzed to identify underlying patterns and then used to train the SARIMAX model. The results show that the SARIMAX model is able to provide accurate sales predictions with a relatively low error rate. Significant seasonal and external factors were also identified, providing valuable insights for business decision-making regarding sales strategy and inventory management. This research concludes that the SARIMAX method is an effective tool for time series data-based product sales prediction. The implementation of this model can assist companies in optimizing business operations and improving competitiveness in the market.]]>
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