Prediction of Employee Attendance Factors Using C4.5 Algorithm, Random Tree, Random Forest

Abstract

Research on the performance of workers based on the determination of standard working hours for absences conducted by workers in a certain period. In disciplinary supervision, workers are expected to be able to provide the best performance in the implementation of work in accordance with predetermined working hours. The measurement of the level of discipline of admission hours for placement workers is carried out every working day, continuously and continuously. Attendance monitoring already uses online attendance by using data downloaded from the online attendance provider as the main data. In addition, data collection is done by filtering employee absentee data and supporting information on the categories that cause mismatches in meeting work schedules. Mobilization of workers according to location and working hours has been regulated in company regulations allowing the placement of workers in accordance with the residence so as not to affect the desired work results the company is still within reasonable limits and can be increased. The assessment of this study as a progression factor inhibiting the company in achieving company targets. From the results of the author's analysis of the prediction of employee delay factors using three algorithms, namely the C.45 algorithm accuracy = 79.37% and AUC value = 0.646, Random Forest Algorithm accuracy = 78.58% and AUC value = 0.807 while for the Random Tree algorithm accuracy = 76.26% and the AUC value = 0.610.

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

“…The results of previous studies, with Random forest model to predict employees with these 10 features through random forests produces more accurate and precise, with and 89% accuracy and 72% precision [13]. From the results of previous research analysis of predictions employee delay factor using three algorithms, namely the accuracy of the C.45 = 79.37% and AUC value = 0.646, Random Forest Algorithm Accuracy = 78.58% and AUC value = 0.807 while for random tree algorithm accuracy = 76.26%and AUC value = 0.610 [14]. By using Random forest can identify whether run broiler breeders lay eggs or not on a certain day during the egg-laying period with an accuracy of about 85% [15].…”