CLUSTERING KUALITAS KINERJA PEGAWAI PADA NARUNA CAFE & RESTO MENGGUNAKAN ALGORITMA K-MEANS
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Abstract
The employee appraisal system at Naruna Cafe and Resto uses conventional, manual-based appraisal methods which are carried out subjectively by the direct supervisor without a clear evaluation framework so that the appraisal process is often not transparent and lacks objectivity, which causes dissatisfaction and injustice among employees. Clustering techniques can be used as an employee assessment to be more objective, consistent, and based on measurable data. This study aims to apply the K-Means Clustering method, as well as use the K-means algorithm to make it easier to perform calculations, after which the researcher performs processing using RapidMiner to obtain results on the performance quality assessment of Naruna Cafe and Resto employees. This study uses quantitative research methods and collects data based on the quality of employee performance as an object of research. This study produced clusters with very satisfactory work quality of as much as 1 data, clusters with satisfactory work quality of as many as 3 data, clusters with quite satisfactory work quality of as much as 4 data, clusters with unsatisfactory work quality of as much as 1 data, and clusters with unsatisfactory work quality as much as 5 data. From the data processing that has been done, it can be concluded that this research has succeeded in creating a quality group of employee performance at Naruna Cafe and Resto that can be used to view employee performance.
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