The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students

Vinna Rahmayanti Setyaning Nastiti; Zamah Sari; Bella Chintia Eka Merita

doi:10.15575/join.v8i1.917

Authors

Vinna Rahmayanti Setyaning Nastiti Department of Informatics, Universitas Muhammadiyah Malang, Indonesia, Indonesia
Zamah Sari Department of Informatics, Universitas Muhammadiyah Malang, Indonesia, Indonesia
Bella Chintia Eka Merita Department of Informatics, Universitas Muhammadiyah Malang, Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v8i1.917

Keywords:

Classification, Deep Learning, Restricted Boltzmann Machine, SMOTE, Specialization

Abstract

Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error.

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