1 Industrial Engineering S1 Degree, Faculty of Industrial Technology, National Institute of Technology Malang, Indonesia.
2 Industrial Engineering Dipl. III, Faculty of Industrial Technology, National Institute of Technology Malang, Indonesia.
Research Article
World Journal of Advanced Research and Reviews, 2023, 17(01), 1275-1285
Article DOI: 10.30574/wjarr.2023.17.1.0184
Received on 16 December 2022; revised on 27 January 2023; accepted on 29 January 2023
In a production process, various production factors can affect the resulting performance. Employees, as a factor of production, play an important role in the production process, so it is necessary to get a high-quality workforce. In this regard, the recruitment process is very important, so it needs to be carried out strictly and thoroughly. The problem that is often encountered in the recruitment process is that applicant files are still handled manually, which allows errors to occur in the recruitment process. This error will impact the resulting performance because the industry has employees who do not fit the criteria. Therefore, a Decision Support System is needed in the recruitment process so that decision-making can be done quickly and accurately, reducing errors. This research aimed to find a decision support system application in employee recruitment. The recruitment process can be faster, more precise, and optimal because we get prospective employees who fit the required criteria. The method used to achieve the research objectives was the Analytical Hierarchy Process (AHP) to develop weights or priorities. The results obtained in this study were an application that shows the ranking of accepted prospective employees based on assessments in the recruitment process. In addition, the test results showed that 95% of the application could meet the Usability and Efficiency characteristics, and 70% meet the overall characteristics.
Decision Support System; Optimal; Recruitment; Analytical Hierarchy Process (AHP); Employee
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Emmalia Adriantantri and Sri Indriani. Decision support system for employee recruitment optimization. World Journal of Advanced Research and Reviews, 2023, 17(1), 1275-1285. Article DOI: https://doi.org/10.30574/wjarr.2023.17.1.0184