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Research Article · Open access · Published 4 June 2020

Decision Support Model for Employee Selection Based on Data Mining and Fuzzy Logic

Clarissa Elfira Amos Pah1 & Ditdit Nugeraha Utama1

Journal of Computer Science, Volume 16, Issue 5, pp. 686–701 · doi.org/10.3844/jcssp.2020.686.701

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Abstract

This paper explains the use of data mining and fuzzy logic in optimizing decision making for employee recruitment, especially in IT Consultant company. This method addresses the company's need to recruit employees with more objective and accurate by utilizing historical data to find patterns of potential employees for the company. First, classification will be carried out using data mining to determine the predictor attributes of potential employees. These predictor attributes will then be used to arrange fuzzy rule base and fuzzy logic structure which will be used to prioritize employees to be accepted. The results obtained will prioritize employees who are recruited objectively scientifically.

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Open access. This article is licensed under a Creative Commons Attribution 3.0 Licence (CC BY 3.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

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Pah, C. E. A. & Utama, D. N. (2020). Decision Support Model for Employee Selection Based on Data Mining and Fuzzy Logic. Journal of Computer Science, 16(5), 686–701. https://doi.org/10.3844/jcssp.2020.686.701

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Volume 16, Issue 5
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Decision Support Model for Employee Selection Based on Data Mining and Fuzzy LogicJournal of Computer Science · Volume 16, Issue 5 · pp. 686–701 · CC BY 3.0Download