Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry
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 Neur...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/6/187 |
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| author | Ari Primantara Udisubakti Ciptomulyono Berlian Al Kindhi |
| author_facet | Ari Primantara Udisubakti Ciptomulyono Berlian Al Kindhi |
| author_sort | Ari Primantara |
| collection | DOAJ |
| description | 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 (R<sup>2</sup> = 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. |
| format | Article |
| id | doaj-art-22af8f10eb0a4d408e926d2fce215864 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-22af8f10eb0a4d408e926d2fce2158642025-08-20T03:30:24ZengMDPI AGAgriEngineering2624-74022025-06-017618710.3390/agriengineering7060187Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer IndustryAri Primantara0Udisubakti Ciptomulyono1Berlian Al Kindhi2School of Interdisciplinary Management and Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Electrical Automation Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaInconsistencies 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 (R<sup>2</sup> = 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.https://www.mdpi.com/2624-7402/7/6/187bagging systemmachine learningproduction efficiencyrandom forest regressorweight inconsistency |
| spellingShingle | Ari Primantara Udisubakti Ciptomulyono Berlian Al Kindhi Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry AgriEngineering bagging system machine learning production efficiency random forest regressor weight inconsistency |
| title | Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry |
| title_full | Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry |
| title_fullStr | Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry |
| title_full_unstemmed | Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry |
| title_short | Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry |
| title_sort | machine learning performance analysis for bagging system improvement key factors model optimization and loss reduction in the fertilizer industry |
| topic | bagging system machine learning production efficiency random forest regressor weight inconsistency |
| url | https://www.mdpi.com/2624-7402/7/6/187 |
| work_keys_str_mv | AT ariprimantara machinelearningperformanceanalysisforbaggingsystemimprovementkeyfactorsmodeloptimizationandlossreductioninthefertilizerindustry AT udisubakticiptomulyono machinelearningperformanceanalysisforbaggingsystemimprovementkeyfactorsmodeloptimizationandlossreductioninthefertilizerindustry AT berlianalkindhi machinelearningperformanceanalysisforbaggingsystemimprovementkeyfactorsmodeloptimizationandlossreductioninthefertilizerindustry |