Identification of Applicants Who are Hesitant to Decide to Study in Order to Create Strategies to Support Decision-making for Admission Case Study of Chandrakasem Rajabhat University - Sanitphonklang, Chaisiri; Kumsiri, Sattawat (Journal of Science Ladkrabang, 2021)
Nowadays, The rate of admission to higher education was reduced but contrary to the number that the university has announced. Therefore, there is a competition for recruiting students to study in universities, both public and private sectors, with different strategies for each institution. The research was presented this time is another strategy. Finding those who are reluctant to choose to study with a university as a way of persuasion By using machine learning techniques with algorithms, the decision tree was extracted from the applicants to the university. This machine learning uses 6,000 records of past applicants from Chandrakasem Rajabhat University from 2017 to 2019. It was found that the qualifications of applicants that could be classified into a reluctant group were application field, GPA, family income, domicile, school and gender. The classification efficiency was 95.89% compared to SVM, KNN, Naive Bayes with classification efficiency of 87.67%, 85.50%, and 82.63%, respectively.