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Systematic Literature Review of Prediction Techniques to Identify Work Skillset

Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Prediction
  • Data mining
  • Work skillset
  • Skills
  • Accuracy

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Acknowledgement

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

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Correspondence to Ely Salwana .

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Zawawi, N.S., Salwana, E., Zulkifli, Z., Nayan, N.M. (2019). Systematic Literature Review of Prediction Techniques to Identify Work Skillset. In: , 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

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

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