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.
Highlights
Quality workforce in a company is a very important asset to support the company's business goals, both large and small companies (Morgan, 2019)
This paper has explained the detail of data mining process to classify the prospective employee whether will be accepted or rejected based on experimental or historical data
Researcher found that C.4.5 data mining model is the most accurate model to be used to find involved attributes with their importance weight
Summary
Quality workforce in a company is a very important asset to support the company's business goals, both large and small companies (Morgan, 2019). Mammadova and Jabrayilova (2014) conducted a research on employee hiring decision making by applying the Fuzzy method According to them, this method can solve the problems of previous studies (multi-criteria assessment and sequencing) that can use qualitative and quantitative data, eliminate the limit on the number of criteria and the number of experts, using a hierarchical structure of criteria. Azar et al (2013) have done the same approach by using data mining for personnel selection in a commercial bank They compared four classification algorithms of QUEST, CHAID, C5.0 and CART and found that C.5.0 (extension of C4.5) had the best accuracy of 80.43%. Both of Chien and Chen (2008) and Azar et al (2013) using demographic data such as age, sex, marital status, university, degree, mayor, experience year, etc., C.4.5 or C.5.0 have capability in dealing with numerical and categorical data value at one
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