Lecturer Performance Prediction Based on Student Evaluation Data Using a Hybrid K-Means and Random Forest Model
DOI:
10.29303/jppipa.v12i1.14163Published:
2026-01-31Downloads
Abstract
Using a quantitative correlational design, this predictive research was based on secondary EDOM data. The first episode of the school year 2024/2025 served as the data collection period. The target population of this research are the lecturer subjected to students’ evaluations from Universitas Al-Irsyad Cilacap. After processing the data and cleaning and aggregating, a total of 594 records of the lecturer were analyzed with a census technique. K-Means was used to detect the presence of latent patterns of performance in the teaching, professional, personality and social dimensions of the lecturer. The Random Forest model was used to predict the performance category of the lecturer from both the baseline and hybrid models. The results of the study showed that the hybrid models were able to predict with a high measure of accuracy, and of the two, the hybrid model was the most robust when compared to the baseline model with a manual high-defined grouping of performance levels. The baseline model was able to completely and perfectly classify the group, the hybrid model with high performance was able to analyze the data in a general way, revealing a structure of performance that was hidden in the data. This means that, there is greater analytical value to the data. This analysis of EDOM data is of high analytical value. The developing of the hybrid model of lecturer performance analysis provides a positive contribution in data-driven quality assurance and decision-making to higher education. Objectives were met.
Keywords:
Evaluation of teaching Hybrid machine learning K-Means clustering Lecturer performance Random ForestReferences
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