Application of Fuzzy Logic System for Coffee Bean Quality Detection - Baracahya Panata Cendikia Rahayu; Ahmad Farrell Raafii Alaiyya Al-Attas; Imam Yanif (Journal of Applied Science, Technology & Humanities | JASTH, 2026)
Manual assessment of coffee bean quality is often subject to inconsistency and evaluator bias, potentially leading to economic losses for farmers and industry stakeholders. This study proposes an objective simulation model for coffee bean quality evaluation using the Mamdani Fuzzy Logic method implemented in MATLAB Fuzzy Logic Toolbox. A quantitative descriptive approach was adopted, utilizing secondary data synthesized from validated experimental literature. The model incorporates three physical input variables: kadar air (%), color intensity (lux), and bean size (mm), with a single output variable representing the quality score. The system process includes fuzzification using trapezoidal and triangular membership functions, evaluation of 27 IF–THEN rules, aggregation through the MAX operator, and defuzzification using the Centroid method. Simulation results demonstrate that the proposed system effectively classifies coffee bean quality into three categories: Premium, Sedang, and Rendah. Under optimal conditions (11.5% moisture content, bright color, and large bean size), the defuzzified score consistently exceeds 70 (Grade 1), aligning with industry quality standards. The findings confirm that fuzzy logic provides a transparent and reliable decision-support framework capable of transforming ambiguous physical measurements into measurable quality indices for coffee standardization.