Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

• Main Authors: NUR Iriawan, Kartika Fithriasari, Brodjol Sutija Suprih Ulama, Wahyuni Suryaningtyas, Irwan Susanto, Anindya Apriliyanti Pravitasari
• Format: Article
• Language: English
• Published: Universitas Indonesia 2018-06-01
• Series: Jurnal Ilmu Komputer dan Informasi
• Subjects: Bernoulli mixture regression model; Bayesian MCMC; Gibbs Sampling; Bidikmisi
• Online Access: http://jiki.cs.ui.ac.id/index.php/jiki/article/view/536
• ISSN: 2088-7051; 2502-9274

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of  the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model