Classification of Non-Civil Servant Performance Appraisal Using Naïve Bayes Classifier Algorithm

Abstract

Employee performance assessment is a way to measure the level of employee productivity. In the process of assessing the performance of Non-Civil Servants (non-PNS) employees at the Regional Technical Implementation Unit of Education and Training of Cooperatives and Entrepreneurs (UPTD P3W) at this time, it is required to classify data based on several factors to find out whether the employee fits into the eligible category or not as the best employee to become a civil servant (PNS) candidate. The purpose of this research is to make it easier to determine the classification of the performance assessment of non-PNS employees at UPTD P3W using the Naïve Bayes Classifier Algorithm and to determine the level of accuracy in the classification of the performance assessment. In this study, the authors used 498 data as training data and 105 data as testing data for manual testing in Excel and for testing using RapidMiner tools. Based on the analysis in the study, the result of the predictions determines the best employees to become candidates for civil servants quickly and accurately, while from the tests performed by comparing training data and with data testing using RapidMiner tools, the accuracy rate is 84.76%.