MODEL KLASIFIKASI KEPUASAN MAHASISWA TEKNIK TERHADAP SARANA PEMBELAJARAN MENGGUNAKAN DATA MINING

Abstract

Student in one of the stakeholder in a university. Therefore, student’s perception in the quality of learning facilities and infrastructures become important to ensure the university’s performance. The Faculty of Engineering of University of Palangka Raya has not comprehensively evaluated the students’ satisfactory of the learning’s facilities. In this research, methods from data mining approach was implemented to classify whether the students satisfy or not with the quality of the learning’s facility in Engineering Faculty. This research compared three data mining algorithm, Decision Tree C4.5, Support Vector Machine, and Naïve Bayes to obtain the best algorithm for the prediction system. 948 responses were collected, 61% of the respondent were satisfied with the quality of the learning facilities and infrastructures, while 39% of the respondents were dissatisfied. The Decision Tree c4.5 had the best performance with accuracy of 88% and precision of 98% compared to the Naïve Bayes and support vector machine.