Classification talent of employee using C4.5, KNN, SVM

Cecilia Stephanie, Riyanarto Sarno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Employees are one of the important points of driving the company. With the existence of capable human resources, the company has competitive advantage compared to competitors. This research will propose a way to classify employees into 4-box talent management. The classification will use the C4.5 method, K-nearest neighbors (KNN), and Support Vector Machine (SVM). In classification, we use 18 criteria to calculate the performance and potential of the employee. The results of mapping the performance and potential of employees will divide employees into 4 labels, label 1 (needs development or too new to evaluate), label 2 (everyday solid contributor), label 3 (potential to be executive in 3+ years), and label 4 (needs to be an executive in 2 years). The results of the employee classification get the result that the SVM method has the highest accuracy, which is 94.62%, with P-value = 3.20E-11. KNN is a method that has the lowest accuracy of 87.37%.

Original languageEnglish
Title of host publication2019 International Conference on Information and Communications Technology, ICOIACT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages388-393
Number of pages6
ISBN (Electronic)9781728116556
DOIs
Publication statusPublished - Jul 2019
Event2nd International Conference on Information and Communications Technology, ICOIACT 2019 - Yogyakarta, Indonesia
Duration: 24 Jul 201925 Jul 2019

Publication series

Name2019 International Conference on Information and Communications Technology, ICOIACT 2019

Conference

Conference2nd International Conference on Information and Communications Technology, ICOIACT 2019
Country/TerritoryIndonesia
CityYogyakarta
Period24/07/1925/07/19

Keywords

  • 4-box grid talent management
  • C4.5
  • Employee classification
  • K-nearest neighbors (KNN)
  • Support Vector Machine (SVM)

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