Design of Decision Support System for Mapping Student Interest in Information Technology and Computing Specialization

Authors

  • Sri Sumarlinda  Faculty of Computer Science, University/of of Duta Bangsa, Surakarta, Indonesia
  • Wiji Lestari  Faculty of Computer Science, University/of of Duta Bangsa, Surakarta, Indonesia

DOI:

https://doi.org//10.32628/CSEIT195670

Keywords:

Decision Support Systems, Mapping, Student Specialization, Learning Styles, Multiple Intelligences

Abstract

The mapping of students will help the selection of student specialization in the research field. The purpose of this research is to develop and develop a decision support system to map and provide direction in the suitability of specialization in the field of computer / information technology to students. Development of a decision sThe mapping of students will help the selection of student specialization in the research field. The purpose of this research is to develop and develop a decision support system to map and provide direction in the suitability of specialization in the field of computer / information technology to students. Development of a decision support system design to map computer students with regard to the types of specialization in IT and Computing specialization. The specialization of students is divided into 8 specializations. The specialization is then correlated with indicators namely interests, grade of subjects, learning styles, multiples intelligences and cognitive styles. The leaning styles according to Memeletics Learning Styles Inventory consist 7 types namely : Visual, Aural, Verbal, Physical, Logical, Social and Solitary. The Multiple Intelligences according to Gardner's Multiple Intelligences Scale consist 8 types namely : Linguistics Intelligence, Logical Mathematics Intelligence, Visual Spatial Intelligence, Bodily Kinesthetics Intelligence, Musical Intelligence, Interpersonal Intelligence, Intrapersonal Intelligence and Naturalist Intelligence. The Cognitive Styles according to Martin Cognitive Styles Inventory consist 5 types namely: Systematic Style, Intuitive Style, Integrated Style, Undifferentiated Style and Split Style.upport system design to map computer students with regard to the types of specialization in IT and Computing specialization. The specialization of students is divided into 8 specializations. The specialization is then correlated with indicators namely interests, grade of subjects, learning styles, multiples intelligences and cognitive styles. The leaning styles according to Memeletics Learning Styles Inventory consist 7 types namely : Visual, Aural, Verbal, Physical, Logical, Social and Solitary. The Multiple Intelligences according to Gardner.’s Multiple Intelligences Scale consist 8 types namely : Linguistics Intelligence, Logical Mathematics Intelligence, Visual Spatial Intelligence, Bodily Kinesthetics Intelligence, Musical Intelligence, Interpersonal Intelligence, Intrapersonal Intelligence and Naturalist Intelligence. The Cognitive Styles according to Martin Cognitive Styles Inventory consist 5 types namely: Systematic Style, Intuitive Style, Integrated Style, Undifferentiated Style and Split Style.

References

  1. Robledo, L.F., sepulveda, J., Archer, S. 2013. Hybrid Simulation Decision Support System for University Management. Proceedings of the 2013 Winter Simulation Conference, 978-1-4799-2076-1/13/$31.00 ©2013 IEEE.
  2. Mabu, A.M., Muhammad, F.A. 2016. An Admission Decision Support System for Nigerian Universities. International Journal of Computer Applications (0975 - 8887) Volume 133 - No.2, January 2016.
  3. Vohra, R. Das, N.N. 2011. Intelligent Decision Support Systems for Admission Management IN Higher Education Institutes. International Journal of Artificial Intelligence & Applications (IJAIA), Vol.2, No.4, October 2011.
  4. Soriano, L.T., Capucao, J.N.B., Palaoag, T.D. 2018. Decision Support Model for Managing AcademicWorkload in State Universities and Colleges. International Journal of Information and Education Technology, Vol. 8, No. 7, July 2018.
  5. Cabrerizo, F.J., Molinera, J.A.M, Perez, C.J., Gijon, J.L., Viedma, E.H. 2015. A Decision Support System to Develop a quality Management in Academic Digital Libraries. Information Sciences 323 (2015) 48-58.
  6. Shobaki, Al., Mazen, J., Naser, A., Samy, S. 2017. Requirements for Applying Decision Support Systems in Palestinian Higher Education Institutions - Applied Study on Al - Aqsa University in Gaza. International Journal of Information Technology and Electrical Engineering, Volume 6, Issue 4 August 2017.
  7. Elnashar, E.A., Heggi, A. 2017. Decision Support System for Higher Education Research examiners & Reviewers in Egyptian. Innovation and Entrepreneurship, ISSN 1314-9253 Volume V, number 3, 2017.
  8. Saptarini, N.G.A.P.H., Prihatini, P.M. 2015. Decision Support System for Scholarship in Bali State Polytechnic Using AHP and TOPSIS. International Conference On Information Technology And Business, ISSN 2460-7223 20th -21th August 2015.
  9. Sudarma, M., Sudana, A.G.K.O., Cahya, I. 2015. Decision Support System for the Selection of Courses in the Higher Education using the Method of Elimination Et Choix Tranduit La Realite. International Journal of Electrical and Computer Engineering (IJECE) Vol. 5, No. 1, February 2015, pp. 129~135. ISSN: 2088-8708.
  10. Kostoglou, V., L Vassilakopoulos, M., Ploskas, N. 2013. Towards A Web Based Decision Support System for Choosing Higher Education Studies. XI Balkan Conference on Operation Research, 07-10 September 2013.
  11. Livieris, I.E., Mikropoulos, T.A., Panagiotis Pintelas, P. 2016. A decision support system for predicting students’ performance. Themes in Science & Technology Education, 9(1), 43-57, 2016.
  12. Pratiwi, D., Lestari, J.P., Agushinta, D.R. 2014. Decision Support System to Majoring High School Student Using Simple Additive Weighting Method. International Journal of Computer Trends and Technology (IJCTT) - volume 10 number 3 - Apr 2014.
  13. Gurkut, C., Muesser Nat, M. 2018. Important Factors Affecting Student Information System Quality and Satisfaction. EURASIA Journal of Mathematics, Science and Technology Education, 2018 14(3):923-932.
  14. Ardana, I.M, Ariawan, I.P.W, Divayana, D.G.H. 2016. Development of Decision Support System to Selection of the Blended Learning Platforms for Mathematics and ICT Learning at SMK TI Udayana. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 5, No. 12, 2016.
  15. El-Quliti, S.A., Ragab, A.H.M., Abdelaal, R., Mohamed, A.W., Mashat, A.S., Noaman,A.Y., Altalhi, A.H. 2017. Strategic Decision Support System Based Hybrid Models for Colleges Enrollment Capacity Planning, Design & Implementation. The Online Journal of Science and Technology - April 2017 Volume 7, Issue 2.
  16. Sumarlinda, Sri. 2013. Sistem Pendukung Keputusan Penentuan Plafon Kredit Dengan Fuzzy MADM (Multiple Attribute Decissio Making) Menggunakanan Metode SAW (Simple Additive Weightin) Di PD.BPR BKK Boyolali Cabang Simo. DutaCom Journal , Vol. 5.
  17. Sumarlinda, Sri. 2017. Sistem Pendukung Keputusan Progresivitas Karyawan dengan Metode Fuzzy di STMIK Duta Bangsa Surakarta. DutaCom Journal, Volume 11, Isue 1, pp 23-42.
  18. Lestari, W., Purnomo, S. 2013. Aplikasi Algoritma Competitive Network untuk Clustering Minat Mahasiswa Terhadap Topik-Topik Penelitian. Duta.Com, Jurnal Ilmiah Teknologi Informasi dan Komunikasi, ISSN: 2086-9436, hal : 37 - 44.
  19. Shovon, M.H.I., Haque, M. 2012. An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree. International Journal of Advanced Computer Science and Applications, Vol.3, No. 8, 2012.
  20. Oyelade, O. J, Oladipupo, O. O, Obagbuwa, I. C. 2010. Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance. International Journal of Computer Science and Information Security, Vol. 7, No. 1, 2010.
  21. Facca, T.M., Allen, S.J. 2011. Using Cluster Analysis to Segment Students Based on Self-Reported Emotionally Intelligent Leadership Behaviors. Journal of Leadership Education, Volume 10, Issue 2 - Summer 2011.
  22. Purnomo, S., Lestari, W., Hastuti, I. 2017. Expert System Design for Guidance of Information Technology (IT) Technopreneurships Based on Students Personal Characteristics. International Journal of Engineering Research and Technology (IJERT), ISSN : 2278-0181.
  23. Lestari, Wiji. 2014. Sistem Clustering Kecerdasan Majemuk Mahasiswa Menggunakan Algoritma Self Organizing Maps (SOM). Jurnal Saintech, Jurnal Penelitian dan Pengabdian Masyarakat, ISSN: 2355-5009, hal : 52 - 58
  24. Purnomo, S., Lestari, W., Hastuti, I. 2016. The Mapping The Information Technology (IT) Technopreneurships Based of Learning Styles and Multiple Intelligence. Paper Proceedings International Conference of Education and Technology for Empowering Agents of Change ISSN : 2541-1578.
  25. Lestari, Wiji. 2015. Pemetaan Gaya Belajar Mahasiswa dengan Clustering Menggunakan Fuzzy C-Means. Jurnal Saintech, Jurnal Penelitian dan Pengabdian Masyarakat, ISSN: 2355-5009, hal : 95 - 102.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-12-30

Issue

Volume 6, Issue 1, January-February-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sri Sumarlinda, Wiji Lestari, " Design of Decision Support System for Mapping Student Interest in Information Technology and Computing Specialization, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 1, pp.44-50, January-February-2020. Available at doi : https://doi.org/10.32628/CSEIT195670