Implementation of Data Mining in Measuring Student Satisfaction at IAIN Kerinci
Abstract
Measuring student satisfaction is crucial, especially considering the increasing competition in the field of education along with the advancement of knowledge and technology. It is essential to assess whether the services expected by students align with the services they actually receive. Evaluating student satisfaction can significantly help higher education institutions improve service quality, which in turn may lead to an increase in student enrollment.This study employs a quantitative method using one of the data mining techniques—classification—through the C4.5 algorithm to measure student satisfaction levels. The population of this research includes active students at IAIN Kerinci, with a sample size of 100 respondents. The students serve as the subjects providing evaluations or opinions on variables characterized by Tangibles, Reliability, Responsiveness, Assurance, and Empathy.The data is processed using data mining classification techniques, with testing conducted through RapidMiner software. The results of the analysis and testing indicate that data mining effectively classifies the variables in measuring student satisfaction, generating 10 decision tree rules with an accuracy rate of 98.22%. These resulting rules are expected to serve as a foundation for making informed decisions on actions needed to enhance student satisfaction.
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References
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