Skip to main content
SpringerLink
Log in
Book cover

International Visual Informatics Conference

IVIC 2019: Advances in Visual Informatics pp 415–428Cite as

Systematic Literature Review of Prediction Techniques to Identify Work Skillset

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11870))

Abstract

A mismatch of skillsets is a main cause to the unemployment in Malaysia. It is a situation where the level and work skillset that are available do not match the market demands and the individual does not know how to identify the skills that they have. To deal with this problem, prediction techniques is used to assist in identifying work-appropriate skills for individual. Thus, a systematic literature review (SLR) on predicting work skillsets using prediction techniques is proposed. The aim of this study is to give an overview on the prediction techniques that have been used to predict work skillset and the accuracy of the techniques. We use SLR to identify 383 prediction techniques studies for identifying skills published from 2014 to 2019. As a result, 9 studies report adequate information and methodology according to our criteria and apply. From the studies, classification techniques are used for predicting work skillset. The algorithms used is Random Forest with precision is 99%. From this study, a future study will be conducted by developing a prediction model to help identifying appropriate work skillsets to meet current needs and identifying the levels of skills they have. The significant of this study is the researchers are able to understand deeply about the prediction techniques used to identify work skillset and the accuracy of the techniques used.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Batool, B.F.: Emotional intelligence and effective leadership. J. Bus. Stud. Q. 4(3), 84–94 (2013)

    Google Scholar 

  2. Ballalle, R., Himendra, B.: Fourth industrial revolution and future of workforce. Int. J. Adv. Res. Ideas Innov. Technol. 4(5), 151–153 (2018)

    Google Scholar 

  3. Ibrahim, M.Z., Ab Rahman, M.N., Yasin, R.M.: Ketidaksepadanan Kemahiran dan Kolaborasi Industri-Institusi PLTV di Malaysia: Satu Cadangan Penyelesaian. SkillsMalaysia J. 3(1), 17–22 (2017)

    Google Scholar 

  4. CEDEFOP.: Skill Mismatch: More Than Meets the Eye. Retrieved from European Centre for the Development of Vocational Training (Cedefop) Homepage. http://www.cedefop.europa.eu/files/9087_en.pdf. Accessed 15/21 2019

  5. Razak, T.R., Hashim, M.A., Noor, N.M., Halim, I.H.A., Shamsul, N.F.F.: Career path recommendation system for UiTM Perlis students using fuzzy logic. In: 2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future, ICIAS 2014 - Proceedings, pp. 1–5. IEEE, Kuala Lumpur (2014)

    Google Scholar 

  6. Sagar, P., Prinima, P., Indu, I.: Analysis of prediction techniques based on classification and regression. Int. J. Comput. Appl. 163(7), 47–51 (2017)

    Google Scholar 

  7. Mallafi, H., Widyantoro, D.H.: Prediction modelling in career management. In: Proceedings - CYBERNETICSCOM 2016: International Conference on Computational Intelligence and Cybernetics, pp. 17–21 (2017)

    Google Scholar 

  8. Chaudhari, H., Yadav, N., Shukla, Y.: A predictive analysis on job recruitment. In: International Conference on Recent Trends in Engineering, Science & Technology - (ICRTEST 2016), pp. 1–5. IET (2018)

    Google Scholar 

  9. Wimmer, H., Powell, L.M.: A comparison of open source tools for data science. J. Inf. Syst. Appl. Res. 9(2), 1–9 (2016)

    Google Scholar 

  10. Xu, H., Yu, Z., Yang, J., Xiong, H., Zhu, H.: Talent circle detection in job transition networks. In: KDD 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 655–664. ACM, New York (2016)

    Google Scholar 

  11. Parmar, K., Vaghela, D., Sharma, P.: Performance prediction of students using distributed data mining. In: 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–5. IEEE, Coimbatore (2015)

    Google Scholar 

  12. Burman, I., Som, S.: Predicting students academic performance using support vector machine. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 756–759. IEEE, Dubai (2019)

    Google Scholar 

  13. Mishra, T., Kumar, D., Gupta, S.: Students’ employability prediction model through data mining. Int. J. Appl. Eng. Res. 11(4), 2275–2282 (2016)

    Google Scholar 

  14. Putpuek, N., Rojanaprasert, N., Atchariyachanvanich, K., Thamrongthanyawong, T.: Comparative study of prediction models for final GPA score: a case study of Rajabhat Rajanagarindra University. In: Proceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018, pp. 92–97. IEEE, Singapore (2018)

    Google Scholar 

  15. Takci, H., Gurkahraman, K., Yelkuvan, A. F.: Measurement of the appropriateness in career selection of the high school students by using data mining algorithms: a case study. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 113–117. IEEE, Prague (2017)

    Google Scholar 

  16. Choudhary, S.: Collaborative job prediction based on Naive Bayes classifier using python platform. In: 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp. 302–306. IEEE, Bangalore (2016)

    Google Scholar 

  17. Ciolacu, M., Tehrani, A.F., Beer, R., Popp, H.: Education 4.0-fostering student’s performance with machine learning methods. In: 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), pp. 432–437. IEEE, Constanta (2017)

    Google Scholar 

  18. Appel, R., Fuchs, T., Dollar, P., Perona, P.: Quickly boosting decision trees - pruning underachieving features earl. In: Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA (2013)

    Google Scholar 

  19. Amra, I.A.A., Maghari, A.Y.A.: Students performance prediction using KNN and Naïve Bayesian. In: ICIT 2017 - 8th International Conference on Information Technology, Proceedings, pp. 909–913. IEEE, Amman (2017)

    Google Scholar 

  20. Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., Linkman, S.: Systematic literature reviews in software engineering - a tertiary study. Inf. Softw. Technol. 52(8), 792–805 (2010)

    Article  Google Scholar 

  21. Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in Software Engineering. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29044-2

    Book  MATH  Google Scholar 

  22. Bharambe, Y., More, N., Mulchandani, M., Shankarmani, R., Shinde, S.G.: Assessing employability of students using data mining techniques. In: 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, pp. 2110–2114. IEEE, Udipi (2017)

    Google Scholar 

  23. Thakar, P, Mehta, A.: A unified model of clustering and classification to improve students’ employability prediction. Int. J. Intell. Syst. Appl. 9(9), 10–18 (2017)

    Google Scholar 

  24. Sharma, M., Goyal, A.: An application of data mining to improve personnel performance evaluation in higher education sector in India. In: 2015 International Conference on Advances in Computer Engineering and Applications, pp. 559–564. IEEE, Ghaziabad (2015)

    Google Scholar 

  25. Katore, L.S., Bhakti, S.R., Jayant, S.U.: Novel Professional career prediction and recommendation method for individual through analytics on personal traits using C4. 5 algorithm. In: 2015 Global Conference on Communication Technologies (GCCT), pp. 503–506. IEEE, Thuckalay (2015)

    Google Scholar 

  26. Arafath, Y.: Predicting career using data mining. In: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 889–894 (2018)

    Google Scholar 

  27. Piad, K.C., Dumlao, M., Ballera, M.A., Ambat, S.C.: Predicting IT employability using data mining techniques. In: 2016 3rd International Conference on Digital Information Processing, Data Mining, and Wireless Communications, DIPDMWC 2016, pp. 26–30. IEEE, Moscow (2016)

    Google Scholar 

  28. Sa, C.L., Hanani, D., Hossain, E.D., Hossin, M.: Student performance analysis system (SPAS). In: The 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), pp. 1–6. IEEE, Kuching (2015)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the Research University Grant by Universiti Kebangsaan Malaysia: Geran Galakan Penyelidik Muda (grant number GGPM-2018-019).

Author information

Authors and Affiliations

  1. Institute of Visual Informatics, University Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia

    Nurul Saadah Zawawi, Ely Salwana & Norshita Mat Nayan

  2. Department of Information Systems, Kulliyyah of Information and Communication Technology, International Islamic University Technology, Gombak, Malaysia

    Zahidah Zulkifli

Authors
  1. Nurul Saadah Zawawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ely Salwana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zahidah Zulkifli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Norshita Mat Nayan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ely Salwana .

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Halimah Badioze Zaman

  2. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  3. National Central University, Taoyuan City, Taiwan

    Timothy K. Shih

  4. Carlos III University of Madrid, Madrid, Spain

    Sergio Velastin

  5. Toyo University, Tokyo, Japan

    Tada Terutoshi

  6. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Nazlena Mohamad Ali

  7. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Mohammad Nazir Ahmad

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Cite this paper

Zawawi, N.S., Salwana, E., Zulkifli, Z., Nayan, N.M. (2019). Systematic Literature Review of Prediction Techniques to Identify Work Skillset. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2019. Lecture Notes in Computer Science(), vol 11870. Springer, Cham. https://doi.org/10.1007/978-3-030-34032-2_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34032-2_37

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34031-5

  • Online ISBN: 978-3-030-34032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation