Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry - Ari Primantara; Udisubakti Ciptomulyono; Berlian Al Kindhi (AgriEngineering)
Inconsistencies in product weight during fertilizer bagging can lead to material losses and reduced operational efficiency. This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). The dataset used consisted of nine numeric sensor variables. Among the models, RFR achieved the highest predictive accuracy (R2 = 0.9638, RMSE = 0.0496, MAE = 0.0338). Feature importance analysis identified the clamping time and air pressure as the most influential variables. A Smart Bagging System was developed using the RFR model, integrating real-time monitoring and automated parameter adjustment. The simulation results show that the system can reduce overweight losses by up to 95%, with potential annual savings of approximately IDR 29 billion. While promising, these results are based on controlled conditions and a limited dataset; further field validation is recommended. The proposed system demonstrates the potential of machine learning to support cost-efficient, real-time process control in industrial bagging operations. This work aligns with SDG 9 and SDG 12 by promoting industrial innovation and reducing resource waste.