The application of data mining techniques for predicting education to new undergraduate students at Chiang Mai Rajabhat University

This study aimed to predict the number of new students enrolled in bachelor's degree programs using data mining techniques. The population and sample consisted of 31,918 applicants for undergraduate studies at Chiang Mai Rajabhat University only for the regular semester in the quota applied and screened for the year 2014-2019. There were two components in the tool used in this study: hardware and software. that uses RapidMiner Studio 9 to analyze data with data mining. The association rules technique of the Apriori model was used to identify the association rules of recruitment behavior in each field of applicants in each group. both general and quota The results of the analysis evaluated the support-based correlation rule model. confidence and an increase from a total of 340 correlation rules considering support = 0.01, confidence = 95%, and a significant addition of more than 1 correlation rule 89.