Decision Tree Algorithm to Measure Employee Performance Discipline

Authors

  • Linda Marlinda STMIK Nusa Mandiri Jakarta
  • Evita Fitri Universitas Nusa Mandiri, Jakarta
  • Siti Nurhasanah Nugraha Universitas Nusa Mandiri, Jakarta
  • Faruq Aziz Universitas Nusa Mandiri, Jakarta
  • Santoso Setiawan Universitas Nusa Mandiri, Jakarta

DOI:

10.33395/sinkron.v7i4.11796

Keywords:

Data Mining, Decision Tree Algorithm, Employee Performance, Knime

Abstract

Performance appraisal is done to measure the performance of an employee on the work done. The company conducts performance appraisals on employees at least every six months, involving all employees. This study uses the Absenteeism_at_work dataset. The purpose of this research is to analyze the performance of the Decision Tree algorithm in the classification process. Classification will be grouped into two, namely: disciplined and undisciplined The classification process will be carried out using K-Nime. Algorithm performance measurement using Knime Analytics Platform is open-source software for creating data science models. Knime builds data understanding and designs data science workflows and reusable components using accuracy, recall, and precision parameters. From the research conducted, the results of the Decision Tree algorithm have an accuracy rate of 94.6% while the label No. 5.4%. Based on the nineteen attributes proposed, it can be concluded that the Decision Tree algorithm has better performance.

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References

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How to Cite

Marlinda, L., Fitri , E. ., Nugraha , S. N. ., Aziz, F., & Setiawan , S. . (2022). Decision Tree Algorithm to Measure Employee Performance Discipline. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(4), 2223-2230. https://doi.org/10.33395/sinkron.v7i4.11796

Issue

Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022

Section

Articles

License

Copyright (c) 2022 Linda Marlinda

Creative Commons License

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

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