Analisis Dan Implementasi Data Mining Untuk Menentukan Penilaian Kinerja Karyawan - Rilvani, Elkin; Surojudin, Nurhadi (Jurnal SIGMA, 2023)
A company must be selective in conducting employee assessments in order to retain employees with the best performance. In assessing employee performance, it can be seen from diligence and discipline. But in reality, employees' good performance sometimes gets bad reviews and even a warning from their superiors. This is caused by the employee assessment monitoring system used, namely only personal assessment without using an assessment system and the data collected is not optimal. Seeing the problems above, the author conducted research using the Naive Bayes method to carry out data processing using data mining algorithms to obtain predictions that can be used as additional references in employee performance assessment decisions for contract extensions. Naive Bayes is a data processing algorithm that is classified as a calculation that is easy to understand but whose accuracy results are reliable. The author also uses the Rapidminer supporting application to test the accuracy of the system created. Testing was carried out by preparing 320 data and testing data of 50 randomly selected data. The testing data will be analyzed using the Rapidminer supporting application. The test results produced an accuracy of 83.96%. Keywords: Employees, Assessment, Data Mining, Naïve Bayes Algorithm