Naive bayes classification and rapidminer application for analysis of lecturer institution's performance.

Customer satisfaction is an essential measure that may be used to compare service performance obtained and expected by customers. The purpose of this study is to analyze lecturer satisfaction with an institution's performance using artificial intelligence approaches. The dataset was produced via observations and questionnaires distributed to instructors. The solution approach was data mining classification with the naive Bayes algorithm. In the analysis process, Rapidminer software is employed. Based on the results of the final test with the traits of Alertness (criterion 1), Empathy (criterion 2), Reliability (criterion 3), and Responsibility, the accuracy rate was 85.48 percent, with a Precision value of 81.08 percent and a Recall value of 93.75 percent (criterion 4). [ABSTRACT FROM AUTHOR]