Penerapan Algoritma Regresi Linear Berganda untuk Prediksi Produksi Rokok pada Pabrik Rokok CARI

Abstract

Production planning is an essential aspect of the manufacturing industry as it determines the appropriate production quantity to meet market demand and operational conditions. At Pabrik Rokok CARI, production planning is still primarily based on available inventory without optimal utilization of historical data, which may lead to discrepancies between production output and actual demand. This study aims to apply the Multiple Linear Regression method to predict cigarette production using historical data on sales, inventory, and working hours. The dataset consisted of 352 daily records, which were divided into 281 training data (80%) and 71 testing data (20%). The resulting regression model was Y = -24.0255 + (0.3838 × X₁) + (0.0015 × X₂) + (78.0807 × X₃), where X₁ represents sales, X₂ represents inventory, and X₃ represents working hours. Model evaluation produced a coefficient of determination (R²) of 87.51%, a Mean Squared Error (MSE) of 10,688.72, a Root Mean Squared Error (RMSE) of 103.39, and a Mean Absolute Error (MAE) of 73.22. These results indicate that the Multiple Linear Regression method provides good predictive performance for estimating cigarette production and can serve as a reliable basis for supporting production planning. The prediction model was further implemented in a web-based application to facilitate historical data processing and provide production prediction results for management decision support.