Machine Learning Model to Determine Dominant Features in Palm Kernel Cake Quality

Palm kernel cake (PKC), a by-product of palm oil production, has the potential to serve as a cost-effective alternative to traditional feed ingredients such as soybeans, owing to its nutritional value and affordability. However, its quality can vary significantly, impacting its suitability for different livestock. This study aims to develop a machine learning model to identify the dominant features influencing PKC quality using data from proximate analysis. Our model utilizes feature selection techniques and machine learning algorithms with a decision tree to determine the critical factors affecting key quality parameters, such as fat, dry matter, crude protein, crude fiber, and ash. We explore the relationship between these features and PKC suitability for different animal diets. Our novel approach sheds light on the intricate factors governing PKC quality, enabling improved processing and utilization of this valuable resource.