<![CDATA[The problem of high employee turnover in a company has several negative impacts in terms of cost, energy, and time and one of them is felt by the fictitious Company “XYZ”. The purpose of this research is to classify the causes of employee turnover in the industry using a classification machine learning model on two different algorithms namely Random Forest and Decision Tree. In addition, this study addresses the implications of previous classification research, employee classification in the education industry, which suggests comparing the evaluation of two machine learning model performances. There are 10 variables and 9,540 historical employee data used in the research. The research technique or method used is Cross-industry Standard Process for Data Mining (CRISP-DM). The results of this study show that the Random Forest classification model is the optimal machine learning model with an AUC - ROC value reaching 0.9988. RapidMiner was used to revalidate the performance of the machine learning model using the same parameters and resulted in the highest accuracy value of 85.04% for the Random Forest model compared to the Decion Tree model.]]>
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