APPLICATION OF ARTIFICIAL INTELLIGENCE IN DETERMINING STUDENT SATISFACTION LEVEL: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS IN HIGHER EDUCATION ACADEMIC SERVICES

Abstract

Student satisfaction is a strategic indicator for evaluating the quality of higher education services; however, conventional measurement through questionnaire surveys has limitations in terms of scalability, objectivity, and analytical speed. This study aims to examine the application of Artificial Intelligence (AI), particularly machine learning algorithms, in predicting and determining student satisfaction levels toward academic services in higher education institutions. The research employs a quantitative approach with a comparative study design involving four classification algorithms—Random Forest, Support Vector Machine (SVM), Naïve Bayes, and Artificial Neural Network (ANN)—applied to satisfaction survey data from 452 active student respondents. The data were processed through preprocessing, feature extraction, model training, and 10-fold cross validation stages. The results show that the Random Forest model achieved the highest accuracy at 91.4%, followed by ANN at 89.7%, SVM at 87.2%, and Naïve Bayes at 83.5%. Feature importance analysis revealed that lecturer quality, administrative service speed, and digital learning facilities were the most dominant factors influencing student satisfaction. This study concludes that the application of AI can improve the accuracy, efficiency, and objectivity of measuring student satisfaction compared to conventional methods, and can serve as a foundation for data-driven decision making by higher education management in formulating service quality improvement policies.