<![CDATA[The evaluation of training participant satisfaction at the Center for Vocational Training and Productivity Development (BBPVP) has traditionally relied on conventional methods, resulting in less accurate and unstructured outcomes. The core issue necessitates a data-driven solution to enhance objectivity and reliability. This study aims to develop a C5.0 algorithm-based classification model to automatically measure participant satisfaction levels and identify dominant influencing factors. The methodology includes collecting survey data from 300 respondents across five SERVQUAL attributes (reliability, assurance, responsiveness, empathy, tangibles), data preprocessing, dataset splitting (80:20), and model development using Python’s Scikit-learn library. Results indicate a model accuracy of 98.3% (12% higher than Naïve Bayes), with "assurance" as the most influential attribute (gain ratio: 0.638). Contributions of this research include: (1) providing BBPVP with an accurate data-driven satisfaction evaluation tool, (2) offering strategic recommendations to improve training quality, particularly in assurance, and (3) potential adoption of this method as a national vocational training evaluation standard.]]>
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