Open AccessOn the modeling of the new student acceptance status through science and technology written test using bernoulli mixture model
Author(s) -
D. P. Shiela Novelia,
Ismaini Zain,
Nur Iriawan,
Wahyuni Suryaningtyas
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1538/1/012062
Subject(s) - test (biology) , gibbs sampling , markov chain monte carlo , mathematics education , computer science , markov chain , bernoulli's principle , technology acceptance model , covariate , psychology , mathematics , statistics , monte carlo method , artificial intelligence , machine learning , engineering , usability , paleontology , bayesian probability , biology , human–computer interaction , aerospace engineering
This research aimed to model StudentAcceptance Status at the Sepuluh Nopember Institute of Technology (ITS) through the written test of science and technology, using Bernoulli Mixture Model in order to evaluate the new student acceptance status. BMM distribution was established based on the comparisonbetween the students’ scores of the basic abilities, namelyMathematics, Physics, Chemistry, and Biology which correspondedto the majors they had chosen, combined with the Student Acceptance Status (0 and 1). This combination generated two components of Mixture, namely right or wrong. The characteristics of each component were then identified through BMM by involving the covariates of Student Acceptance Status, namely the basic ability test and the scholastic test. The combination of Markov Chain Monte Carlo with the Gibbs Sampling algorithm was employed to estimate the parameters used in this research. This method was applied to the data of prospective students who registered in ITS through written test of science and technology. This research result showed the estimated parameters and the formed model of BMM.