Integrating Random Forest And Forward-Chaining Inference For Automated Coffee Quality Classification Using Sensory Standards
DOI:
https://doi.org/10.30865/ijics.v9i3.9585Keywords:
specialty coffee, Random Forest, Forward Chaining, machine learning, expert system, coffee quality classificationAbstract
The increasing consumption of coffee has driven the need for a fast and consistent coffee quality assessment process. The quality of specialty coffee is generally determined through cupping tests based on sensory attributes; however, this method still relies heavily on panelist subjectivity and requires considerable time and cost. This study aims to develop an automated system for specialty coffee quality classification by integrating the Random Forest algorithm and Forward Chaining inference logic. Random Forest is employed to perform initial classification and identify the importance level of sensory attributes, while Forward Chaining functions as a rule-based system to validate and explain the classification results. The study utilizes 207 coffee sensory profile data samples with 11 attributes based on the Specialty Coffee Association (SCA) cupping standards. The experimental results show that the Random Forest model achieves optimal performance with 100% accuracy, precision, recall, and F1-score, with Total Cup Points identified as the most dominant attribute. The integration of these two methods produces an accurate, consistent, and explainable coffee quality classification system in accordance with SCA standards.
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