| This study aims to implement the Naïve Bayes Classifier algorithm in predicting the level of productivity of employee performance at PT. Focon Indo Concrete. The data used in this study is historical data on employee performance at the company for the last 1 year. Furthermore, the Naïve Bayes Classifier model was applied to the processed dataset to predict the productivity level of employee performance. The implications of the results show that the Naïve Bayes Classifier algorithm can produce predictions that are quite accurate in predicting the level of productivity of employee performance at PT. Focon Indo Concrete. The resulting Naïve Bayes Classifier model has an accuracy of 90.80%, precision of 98.33%, recall of 99.09%, and Area Under Curve (AUC) change score of 0.999. The results of this study can categorize employees into three categories: less productive, moderately productive, and highly productive. Thus, the Naïve Bayes Classifier algorithm can be an alternative in evaluating employee performance and providing recommendations to increase employee performance productivity in the company. |
