Metode k-means clustering untuk mengukur tingkat kedisiplinan pegawai (studi kasus di pemerintah kabupaten padang pariaman)

Rezki -; Sarjon Defit; Sumijan

doi:10.37859/coscitech.v4i1.4728

Rezki - Upi YPTK Padang
Sarjon Defit Universitas Putra Indonesia YPTK Padang
Sumijan Universitas Putra Indonesia YPTK Padang

https://doi.org/10.37859/coscitech.v4i1.4728

Abstract

Knowledge Discovery In Database (KDD) is a process of converting raw data into useful data in the form of information. Data mining is a technique of digging up hidden or hidden valuable information in a very large data collection (database) so that an interesting pattern is found that was previously unknown. Clustering is a method in data mining in which data objects that have similarities or the same characteristics are grouped into one group and those that are different are grouped into another group. One aspect of discipline that can be used to evaluate employee performance is attendance. The k-means method is used to classify employee discipline levels and then describes the values that have been obtained to generate new knowledge regarding data patterns on employee discipline levels. The attendance data is clustered into 3, namely to measure low, medium, and high levels of discipline. After carrying out the calculation process, the 41 employee samples produced 3 iterations, and the final result was 3 clustering, namely cluster 1 of 10 employees with low discipline, cluster 2 of 7 employees with moderate discipline, and cluster 3 of 24 employees with high discipline. This is intended so that leaders can find out which employees have high, medium and low levels of discipline so that they can provide appreciation or rewards and sanctions in order to maintain and improve their discipline so that service to the community can be optimal and the vision and mission of the local government can be achieved.

Keywords: KDD, Data Mining, K-Means Clustering Method, Discipline

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