<![CDATA[The pet food industry has grown rapidly alongside the rising number of pet owners, intensifying competition among stores and making sales-data-driven inventory management increasingly essential, particularly at Pet Shop Marendal, which had only recorded transactions without using them for forecasting. This study aims to develop a forecasting model for pet food sales levels and to identify the factors that most influence them, in order to support stock planning and business decision-making. The method used is Decision Tree Regression with the CART algorithm, applied to 1,500 transaction records from January–June 2025, split with an 80:20 ratio and validated through 5-fold cross-validation. The results show that the model achieves very high accuracy, with an R² of 0.9963 and an RMSE of Rp49,168, while the variables Quantity Sold and Unit Price together account for 98.99% of feature importance. It is concluded that Decision Tree Regression is effective and reliable for short-term sales forecasting, with practical implications providing a basis for stock management strategies and product-mix optimization for pet food business owners.]]>
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