Proposed Framework Based on K-Means Clustering Technique to Provide Recommendations in Designing Job Rotation
DOI:
https://doi.org/10.24002/ijieem.v7i2.10911Keywords:
employee data, job rotation, K-Means, recommendationsAbstract
Designing work rotation (JR) is crucial for a company. It is necessary to design JR based on objective recommendations. With the current development of information technology, it is very possible for companies to store employee data digitally. Additionally, companies can process employee data using data mining techniques. Then the result can be used as a basis for designing JR. This research aims to provide a framework using the K-Means clustering technique to provide recommendations as a basis for designing JR. The proposed framework is implemented in a real case, specifically targeting 490 machine operators and technicians in a cigarette manufacturer in Indonesia. The clustering analysis results reveal a grouping of operators and technicians into five distinct categories. Furthermore, the characteristics of each group can be used as one criterion for providing recommendations for designing JR.
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