Penerapan Algoritma K-Means Clustering Untuk Mengetahui Kemampuan Karyawan IT
Assessment of the ability of IT employees is very necessary to determine the ability of employees to work so that it can be a reference and evaluation for the future. Therefore, we need a technique that can group the employee's ability to determine the employee's ability using the K-Means...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
LPPM Universitas Bina Sarana Informatika
2022-01-01
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| Series: | Computer Science |
| Subjects: | |
| Online Access: | https://jurnal.bsi.ac.id/index.php/co-science/article/view/623 |
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| Summary: | Assessment of the ability of IT employees is very necessary to determine the ability of employees to work so that it can be a reference and evaluation for the future. Therefore, we need a technique that can group the employee's ability to determine the employee's ability using the K-Means Clustering Algorithm method. The data grouping is done in several stages, namely, inputting data into Ms. Excel based on the results of data collection through Google Forms, processing and testing with the K-Means Clustering Algorithm, analysis of results, and grouping employee data with excellent, good, adequate, poor, and very poor skills. From the results of the tests that have been carried out, it is obtained 5 clusters with 2 iterations, namely employees with excellent abilities consisting of 2 members, employees with good abilities consisting of 2 members, employees with sufficient abilities consisting of 1 member, employees with less ability consisting of 2 members. , and employees with less ability consist of 3 members. Based on the results of research conducted at PT. Loka Citra Media, the application of the K-Means Clustering Algorithm can be used to identify and classify IT employees' abilities for the purpose of evaluating employees and forming project teams. The grouping of data obtained from research results and can be considered for evaluating the performance of IT employees with an accuracy value of 40%. The more criteria, the better the results will be obtained. |
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| ISSN: | 2808-9065 2774-9711 |