Abstract
Performance prediction is the forecast of future performance conditions
based on past and present information. Forecasts can be made about companies,
departments, systems, processes, and employees. This study focuses on assessing
employee performance in terms of employee behavior, work, and growth potential.
Organizations benefit when their employees perform well. Therefore, predicting
employee performance plays an important role in a growing organization. To this end,
we propose three machine learning algorithms: a support vector machine, a decision
tree (j48), and a naive Bayes classifier. These can predict employee behavior in the
workplace. Comparing the results, the Naive Bayes algorithm shows better results than
the other two algorithms on the basis of metrics such as timeliness, error loss, and
accuracy.
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