Comparison of Naive Bayes Algorithm and Decition Tree for Employee Classification Predictions Tending to Change Work - Henny Yulianti (Teknik Informatika Universitas Pamulang, 2022)
In recent years due to the uncertain economic conditions and situation of a country, many employees with a certain level of education, work experience, countries with different levels of development and income per capita of the country and several other factors, causing many employees tend to change places of work. Due to the various factors that cause employees to change jobs and advances in information technology, it is also difficult to predict what factors influence employee decision making to move to a new place. Therefore, it is necessary to know what factors and conditions are in employees so that they have a tendency to change jobs, this is necessary so that companies can prevent, anticipate and immediately find other solutions as early as possible if this condition should occur to their employees. Based on the problems and objectives that have been described, this study predicts the classification of employees who have a tendency to change workplaces by integrating the Naive Bayes algorithm and Decision Tree. The aim is to identify the dominant factors that influence employees to change places of work. From the results of the research conducted, it was found that there are three (3) dominant factors that influence employees to change jobs, namely employees with STEM (Science Technology Engineering Mathematical) expertise, company size and education level as well as the highest level of accuracy from the Naive Bayes Algorithm 80.79 and the highest AUC from the Decision Tree Algorithm. 0.822.