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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Arif Rahmat Shaumi
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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PERAMALAN DATA PENJUALAN UNTUK MENGETAHUI TINGKAT PENJUALAN MAKANAN HEWAN PELIHARAAN MENGGUNAKAN METODE DECISION TREE REGRESI Arif Rahmat Shaumi; Sriani
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 5 (2026): October (2026)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/a4999578

Abstract

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.
Co-Authors Sriani