The Application of Business Intelligence and Data Mining Analysis for Predicting Product Sales Trends
Keywords:
Keywords: Business Intelligence, Data Mining, Sales Forecasting, Time Series, BI DashboardAbstract
Rapid advancements in information technology have driven the use of data as the foundation for business decision-making, with Business Intelligence and Data Mining emerging as the primary approaches for effectively processing and analyzing sales data. This study aims to implement the integration of Business Intelligence and Data Mining to accurately predict product sales trends. This study employs a quantitative approach involving the collection of historical sales data, preprocessing, the development of a BI dashboard, and predictive modeling using time series analysis as part of Data Mining techniques. The results show that the BI dashboard is capable of displaying sales trends visually and informatively, while the predictive model produces values close to actual data with an average difference of around 5 million rupiah per period and a low error rate. Seasonal patterns and sales growth trends during specific periods were consistently identified. The integration of Business Intelligence and Data Mining has proven effective in improving the accuracy of sales trend predictions and supporting data-driven decision-making. This research successfully achieved its intended objectives, although further development is still needed by incorporating external variables and more complex prediction methods to improve model accuracy in the future.
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