Perbandingan Model NBC, SVM, dan C4.5 dalam Mengukur Kinerja Karyawan Berprestasi Pasca Pandemi Covid-19

Classifying employee performance appraisals is one way to improve the quality of workers. Employee performance appraisal is very important in determining good employees in a company. The process of appraisal of employee performance is only assessed manually in the absence of an application or system. The algorithm applied to employee performance utilizes the Naïve Bayes Classifier algorithm because it refers to previous research, there are several research findings. Using 310 employee data divided into 5 groups, namely Very High Performance, High Performance, Standard Performance, Low Performance and Ineffective Performance, this test uses the RapidMiner tool version 7.2.0 naïve Bayes Classifier algorithm model resulting in an accuracy rate of 84.52%, the C4.5 algorithm produces an accuracy rate of 74.19% and while using the Support Vector Machine algorithm produces an accuracy rate of 56.13%. If using the WEKA tools version 3.8.0 The Naïve Bayes Classifier algorithm model produces an accuracy rate of 81.93%, the C4.5 algorithm produces an accuracy rate of 75.80% and while using the Support Vector Machine algorithm produces an accuracy rate of 60.32%.