Decision Tree Algorithm to Measure Employee Performance Discipline

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.