Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang

Abstract

Discipline assessment of employee performance is one of the factors to improve the situation of the quality of human resources. Monitoring and assessment of employee discipline must be carried out continuously as some of the characteristics of management that have gone well as a benchmark for considering the targets that have been set. The K-Means method and the Fuzzy C-Means (FCM) method are non-hierarchical cluster methods. Both methods attempt to partition data into one or more clusters, so that data with the same characteristics are grouped into the same cluster or groups and data with different characteristics are grouped into other groups. This study discusses the comparison of the K-Means method and the Fuzzy C-Means (FCM) method in analyzing employee performance at PT. Cemara Khatulistiwa Persada Bontang, where groups of employees with high, medium, and low levels of employee performance will be determined based on the clustering results of the two methods and determine the best method. The grouping of data for the two methods was obtained from employee attendance data in 2020. Based on the results, it was found that clustering using the K-Means method in the first cluster (low performance level) had 23 employees, the second cluster (medium performance level) had 27 employees, and cluster the third (high performance level) there are 30 employees. Then based on the results of clustering using the FCM method in the first cluster (medium performance level) there are 26 employees, the second cluster (high performance level) there are 31 employees, and the third cluster (low performance level) there are 23 employees. Based on the results of the standard deviation ratio, it was obtained that the K-Means method with a value of 2.4944 was better than the FCM method with a value of 2.7323 in clustering employee performance at PT. Cemara Khatulistiwa Persada.