Staff Employment Platform (StEP) Using Job Profiling Analytics

  • Ezzatul Akmal Kamaru ZamanEmail author
  • Ahmad Farhan Ahmad Kamal
  • Azlinah Mohamed
  • Azlin Ahmad
  • Raja Aisyah Zahira Raja Mohd Zamri
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)


Staff Employment Platform (StEP) is a web-based application which employed machine learning engine to monitor Human Resource Management in hiring and talent managing. Instead of using the conventional method of hiring, StEP engine is built using decision tree classification technique to select the most significant skillsets for each job position intelligently, together with classifying the best position. The engine will then rank and predict competent candidate for the selected position with specific criteria. With the ranking method, the weightage of the profile skillset, qualification level and year of experience are summed up. Subsequently, this sum will be resulting in the competency percentage which is calculated by using a Capacity Utilization Rate formula. The proposed formula is designed and tested specifically for this problem. With the accuracy of 63.5% of Decision Tree classification, the integration of machine learning engine and ranking methods using Capacity Utilization Rate in StEP provides a feature to assist the company recruiters in optimizing candidates ranking and review the most competent candidates.


Classification Data analytics and visualization Data science Decision tree Human resources management SAS Viya User profiling 



The authors would like to thank Ministry of Education Malaysia for funding this research project through a Research University Grant; Bestari Perdana 2018 Grant, project titled “Modified Clustering Algorithm for Analysing and Visualizing the Structured and Unstructured Data” (600-RMI/PERDANA 513 BESTARI(059/2018)). Also appreciation goes to the Research Management Center (RMC) of UiTM for providing an excellent research environment in completing this research work. Thanks to Prof Yap Bee Wah for her time in reviewing and validating the result of this paper.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ezzatul Akmal Kamaru Zaman
    • 1
    Email author
  • Ahmad Farhan Ahmad Kamal
    • 1
  • Azlinah Mohamed
    • 1
  • Azlin Ahmad
    • 1
  • Raja Aisyah Zahira Raja Mohd Zamri
    • 1
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia

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