Klasifikasi Kelarisan Produk Makanan–Minuman Menggunakan Decision Tree - Dina Amalia; Whisnu Pamungkas; Fariz Syuhada; Ryan Agung Prasetyo (Ikatan Ahli Informatika Indonesia, 2025)
This study aims to classify the sales performance of food and beverage products using the Decision Tree algorithm. The dataset, obtained from local F&B retail transaction records, includes four key attributes: city, product category, price, and discount percentage, with the target label “Laku” (Yes/No). The research process involves data verification, an 80/20 train–test split, model construction using the Gain Ratio criterion, and performance evaluation through accuracy, precision, and recall metrics in RapidMiner. The resulting model achieved an accuracy of 94.58%, with precision values of 96.53% for the “Laku” class and 89.55% for “Tidak Laku.” The decision rules indicate that price and discount are the most influential factors affecting product sales, where moderate prices and discounts above 10–15% increase the likelihood of products being sold. These findings confirm that Decision Tree provides interpretable and actionable insights for optimizing pricing and promotional strategies. Therefore, the model can serve as a decision-support tool for small and medium F&B enterprises to forecast product performance and enhance marketing decisions.