Machine Learning Analysis Using the Hybrid K-Means and C4.5 Model in Determining Student Readiness for Educational Field Practice

Abstract

The evaluation of student readiness for Educational Field Practice (PLK) has conventionally relied on the subjective assessments of supervising lecturers and mentor teachers, posing a risk of inaccurate outcomes. This traditional framework continues to depend on manual, non-standardized analytical approaches. To address these limitations, this study aims to develop a hybrid K-Means and C4.5 machine learning model to categorize the readiness levels of PLK students at Universitas Negeri Padang. K-Means clustering was initially applied to group evaluation data into three distinct readiness categories, with the resulting cluster labels serving as target variables. Subsequently, the C4.5 algorithm was utilized to extract decision rules and generate a predictive decision tree. The dataset comprises 3,578 student grade records obtained from the Center for Field Experience (Pusat PPL) at Universitas Negeri Padang for the July–December 2025 period, encompassing ten assessment indicator variables ranging from partner school observations to final practice reports. K-Means clustering classified 2,619 students (73.20%) into the "Ready" category, 936 students (26.16%) as "Sufficiently Ready," and 23 students (0.64%) as "Needs Mentoring". The C4.5 model selected the learning media indicator (X4) as the root node, achieving an overall accuracy of 92.32%, a weighted precision of 92.33%, a recall of 92.32%, and an F1-score of 92.33%. These findings demonstrate that the hybrid K-Means–C4.5 approach provides an objective, transparent, and data-driven instrument to support PLK administrators in final evaluations and in determining student readiness status upon completing field practice.