The use of Naïve Bayes classifier to predict the national selection for state university entrance (SNMPTN) acceptance status at statistics study program in Tanjungpura University

• Main Authors: Perdana, Hendra, Satyahadewi, Neva, Tamtama, Ray, Anggriani, Suci
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Classifiers; Colleges & universities; Decision support systems; Seats; Statistical analysis; Students
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0111969

The National Selection for State Entrance or SNMPTN is the most common choice for high school students who are looking for higher education. Statistics is one of the study programs in Tanjungpura University that has a capacity of 20 seats for SNMPTN. Due to the low capacity, the prospective students must plan their best strategy to pass through SNMPTN. By identifying the criteria and determining factors, the decision support system can help the students to prepare themselves to pass the new student admission. In this study, the Naïve Bayes Classifier algorithm was used to predict the acceptance status on SNMPTN at Statistics study program in Tanjungpura University. The data in this study was primary data from questionnaires, and 93 samples were collected from Statistics students. The independent attributes used were a preferred status at Statistics study program of Tanjungpura University, provincial-level achievements, national-level achievements, the average report grades from 10th to 12th grade for some subjects such as mathematics, physics, chemistry, biology, Indonesian, and English. Meanwhile, the dependent attribute used was an acceptance status of SNMPTN at Statistics study program in Tanjungpura University, which was classified as 1 (pass) and 0 (fail). Based on the results of study, the accuracy value obtained was fairly accurate at 78.45%.