Analysis of Factors Influencing Bagging Performance using Supervised Machine Learning Method
Keywords:
Bagging, fertilizer, industry, Random Forest RegressorAbstract
Urea fertilizer is an essential product mainly for people working in an agriculture field. The demand increases every year caused by rapidly growth globalization. This problem obliges fertilizer industry to be more competitive by reducing products losses and production cost. This study introduces supervised machine learning to predict which factors influence fertilizer weight losses during bagging process, such as moisture, products temperature, and wind pressure. Method used in this study is Random Forest Regressor, a well-known method that combines several decision trees into a single output commonly for classification and regression. Factors studied were analysed using Random Forest resulting MSE, R2 and Spearman’s method to determine the correlation between the factors towards fertilizer weight. MSE value obtained from this study was 0.0036 and R2 -0.5334. Low R2 result may be caused by insufficient data. The Spearman coefficients of moisture, products temperature, and wind pressure were 0.035, 0.244, 0.013, respectively. Spearman coefficient shows good result if the value ranging from -1 to +1, which obtained from products temperature. This study shows that Random Forest Regressor can predict several factors that influence productivity of fertilizer industry, particularly in bagging system. However, further research with larger data range is still needed
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