Conference paper Gold Open Access 2019

Recommendation Systems for internship place using artificial intelligence based on competence

Journal of Physics: Conference Series
Conference · Vol. 1165 · Issue 1 · Art. 012007
Abstract

Internship program is important to be implemented as it can increase the competence of the university students. In fact, the program gives significant impact to the success of the students in industry. Students freely choose their suitable place for the internship because suitable place will make students feel motivated and as a result they will be more competent. Industrial internship program is learning at the relevant industry, aimed to increase the competence of the students by introducing them with real working condition. Students are still find difficulty in deciding the suitable place for internship because of their low confidence or competence with related issue. Now, students only need to fill the questionnaire and take the test. The data obtained from the test and questionnaires are then processed by using Artificial Neural Network (ANN), one of the Artificial Intelligence (AI) method. The result was that system can execute the process well and provide accurate recommendation for informatics students to determine suitable place for their internship program. Neural Network in the system is using the recurrent architecture to produce accurate optimal training. © 2019 Published under licence by IOP Publishing Ltd.

Keywords

Author Keywords

Not provided

Index Keywords

Apprentices Students University students Neural networks Internship programs Surveys Industrial internships Optimal training
Author Affiliations
Informatics Management, Universitas Pendidikan Ganesha, Bali, Indonesia
Department of Informatics Education, Universitas Pendidikan Ganesha, Bali, Indonesia
Funding & Acknowledgements
No funding information
References 10 References
1 J Empower, (2017)
2 Jurnal Pendidikan Vokasi, (2015)
3 Kejuruan Teknol Dan, (2016)
4 Nusakini, (2017)
5 E Journal Progr Pascasarj Univ Pendidik Ganesha Progr Stud Adm Pendidik, (2014)
6 Ijccs Indonesian J Comput Cybern Syst, (2014)
7 Isinkaye, Folasade Olubusola, Recommendation systems: Principles, methods and evaluation, Egyptian Informatics Journal, 16, 3, pp. 261-273, (2015)
8 J Kependidikan, (2017)
9 Jurnal Teknik ITS, (2012)
10 J Pendidik Teknol Dan Kejuru, (2016)
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