Comparison Of Clustering Algorithm In Employee Training Management Recommendations

The implementation of training is one way to develop ahar employees who have the ability according to the times and the organization need. Right now, the training process is inefficient on employee needs and hasn't used the results of employees carried out every year. This condition results in a discrepancy between the capabilities of the employees and the expectations of the organization. With the use of the clustering method for grouping employees based on the assessment criteria each year, it is hoped that it can assist in determining the appropriate type of training. The algorithms used in this study are the K-Means Clustering algorithm and the K-Medoids Clustering algorithm. The attribute components used are discipline, loyalty, work performance, responsibility, obedience, honesty, cooperation, initiative, and leadership. The results of this study indicate that the K-Means and K-Medoids algorithms show the same cluster results for the
$\mathrm{k}=3$
value with the K-Means DBI value being slightly better than the K-Medoids.