Analysis of Combination Machine Learning Classification with Feature Selection Technique for Lecturer Performance Analysis Model

Abstract

Machine learning-based classification techniques are widely utilized for accurate analysis in various fields. This study focuses on assessing lecturer performance in higher education to enhance teaching standards and produce high-quality learning outcomes. Previous studies have employed multiparameter approaches, such as statistical correlation analysis, but these methods fail to achieve optimal accuracy and precision due to limited alignment with data characteristics. This research proposes a lecturer performance measurement model by evaluating three machine learning algorithms: k-Nearest Neighbors (k-NN), Decision Tree, and Na¨pve Bayes. The model integrates three feature selection techniques to improve classification performance: ANOVA, Information Gain, and Chi-Square. The study aims to enhance classification accuracy and assess the impact of feature selection techniques on performance metrics. A significant contribution of this research is introducing a dynamic feature selection approach tailored to data characteristics, which improves classification model performance. The methodology comprises three main stages: data loading and measurement of relevant parameters; data preprocessing, including filtering, cleaning, transformation, normalization, and feature selection; and performance evaluation using a machine learning-based classification approach. Experimental results demonstrate that the Decision Tree algorithm combined with Chi-Square feature selection achieved an accuracy of 0.887, precision of 0.903, recall of 0.887, and F1-score of 0.884. The proposed model
provides a reliable framework for evaluating lecturer performance and can be utilized to recognize and reward high-performing lecturers effectively.