The Implementation of Random Forest to Predict Sales a Case Study at Chatime Binjai Supermall - Sandy, Boy; Muliono, Rizki (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
In an increasingly competitive business environment, retail industries like Chatime Binjai Supermall must quickly adapt. Changes in consumer trends, preferences, and technological advancements significantly impact business strategies. To stay competitive, Chatime Binjai Supermall needs to optimize sales, marketing, and inventory management through accurate data analysis and prediction. Random Forest, a powerful machine learning algorithm, is used to process historical data and more accurately predict sales. This study evaluates the performance of Random Forest in predicting daily, weekly, and monthly sales. The analysis shows that products like "Jasmine Green Tea (L)" have the highest daily demand, "PEARL (L)" leads weekly sales, and there is an increase in demand for specific products monthly, such as "CT RAINBOW JELLY (L)." The implementation of the Random Forest algorithm at Chatime Binjai Supermall demonstrates significant potential in enhancing sales efficiency and data-driven decision-making, helping the company remain relevant and competitive amidst market changes.