Implementation of Business Intelligence and Data Mining in Money Changer Transaction Analysis (Case Study of PT. Gemilang Artha Valindo)

This study aimed to implement Business Intelligence (BI) and Data Mining for analyzing currency exchange transactions at PT. Gemilang Artha Valindo to support data-driven decision-making. Transaction data was analyzed using Power BI to generate visualizations, including a pie chart for transaction frequency by currency type, a bar chart for the number of buy and sell transactions per currency, and a line chart for monthly average exchange rate fluctuations. The pie chart indicated that the AUD currency dominated transactions, contributing 51.95% of the total. The bar chart revealed that AUD buy transactions accounted for 63.22% of total AUD transactions, while the line chart showed that GBP and EUR had the highest average exchange rates, reaching Rp20,835 and Rp17,700, respectively. The exchange rate prediction process utilized three algorithms: Linear Regression, K-Nearest Neighbors (KNN), and Random Forest. Their performances were evaluated using Root Mean Squared Error (RMSE). The Random Forest algorithm produced the most accurate predictions with the lowest RMSE value of 134.63, followed by KNN and Linear Regression. These findings highlight the importance of leveraging BI and Data Mining to transform transaction data into valuable insights, enabling more informed business decisions.