Model Regresi Linear untuk Efisiensi Stok dan Prediksi Kebutuhan Bawang Putih Kupas UMKM - Weri Sirait; Nur Azizah; Rahmat Hidayat (LPPM STT Terpadu Nurul Fikri, 2025)
The demand for peeled garlic at the Gilingan Bakso Barokah business tends to fluctuate and be difficult to predict. Inaccurate daily stock determination often leads to problems, especially when peeled garlic stock is excessive while demand is low. Peeled garlic will yellow, rot, and degrade in quality, while customers expect it to be fresh. To overcome this problem, this study aims to predict daily peeled garlic requirements using a simple linear regression model. The data used are daily sales records for peeled garlic from January to December 2024 at Gilingan Bakso Barokah. The linear regression model was built using time as the predictor variable to estimate daily sales trends. The results show that the model is capable of providing reasonably accurate estimates, with a Mean Squared Error (MSE) of 8.93 and a validation score of 9.03. The prediction model projects the peeled garlic requirement over the next 30 days at around 16 kg per day. These findings can help business owners manage peeled garlic stock more efficiently, minimize waste, and maintain customer satisfaction. This research provides an initial, stable, and reliable predictive model for the Gilingan Bakso Barokah business, while simultaneously demonstrating the effectiveness of simple linear regression for the daily management of fresh raw material stocks, with an accuracy level of MSE ≈ 9.