Classification method at acceptance of new student at public university on the national written test

Ika S.W. Antari*, Ismaini Zain, Suhartono

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Acceptance of new students at public universities through the national written test is based on the total score and the capacity of the study program. This causes the study program accepts several students who have low scores on the main subject of the study program. The purpose of this study is to find the best method in predicting the probability of being accepted on the national written test and find the minimum score for each subject that must be achieved by participants to be accepted at a public university. There are two classification methods in statistics that are studied to overcome this problem, i.e. logistic regression and random forest. The results showed that the best logistic regression model had an accuracy of 97.11 percent, whereas the random forest method had an accuracy of 96.59 percent. Furthermore, the minimum score for each subject was developed based on the univariate logistic regression model.

Original languageEnglish
Article number052009
JournalIOP Conference Series: Materials Science and Engineering
Volume546
Issue number5
DOIs
Publication statusPublished - 1 Jul 2019
Event9th Annual Basic Science International Conference 2019, BaSIC 2019 - Malang, Indonesia
Duration: 20 Mar 201921 Mar 2019

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