Employee Education and Training Recommendations using the Apriori Algorithm

Abstract

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data on the implementation of education and training in 2022 in the MoF Central ICT Department, only 27.35% of the employees participated in education and training according to the proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or substitute participants who are not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the a priori algorithm to analyze historical data on employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resources Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.