بکارگیری AHP-TOPSIS فازی جهت تصمیم گیری در فرآیند انتخاب مدیر منابع انسانی

ABSTRACT Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process

High-quality Human Resources (HR) is essential in determining the success of a company. The first step to ensure the availability of a work force to drive a company to success is to conduct an appropriate selection process. According to I.T. Robertson and M. Smith, HR selection process usually involves personality testing, interviewing, and evaluation of the curriculum vitae of candidates [1]. However, the final decision usually involves a large degree of human judgment. A shortcoming of this traditional method is humans are known to be prone to bias, even though more experienced managers tend to be less affected by it [2].

To overcome this, various metrics have been developed and used in the process of candidate selection.

This is the case in one of the largest telecommunication company in Indonesia, which this paper takes as a study case. PT. XYZ has developed quite an elaborate system of metrics for the measurement its employees, including in the case of selecting candidates for promotion. This practice safeguards the candidate selection process in that it is difficult for the committee in charge of the selection process to ignore facts about the candidate’s background and past performance. This would also give a higher guarantee that the candidate selected will more strongly reflect the values of the organization.

However, human judgment still play an important role in making the decisions. Consequently, it is difficult to guarantee that the decisions are consistent and bias-free. Therefore, researchers explore various methods for computer-aided decision making, for example fuzzy logic, decision tree, and rough set theory. The problem of personnel selection is usually framed as a Multi Criteria Decision Making (MCDM), in which the candidates’ performance are measured across various metrics. It is the combining of these scores in which researchers attempt to produce a decision that is close to the actual results.

One of the more popular MCDM methods for personnel selection is the Fuzzy Analytical Hierarchy Process (AHP). In this method, a set of criteria is produced and methodically weighted according to their importance. Candidates are measured based on these criteria and receives a final score which reflects this importance weighting. Another method that is also popular is the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In this method, characteristics of the candidates are compared to that of an ideal. This mimics a common thought process in humans, in which people evaluate things not based on some criteria, but on comparison with an ideal instance of the same type.

To gain the benefits of these methods, this paper applies a combination of both: the Fuzzy AHP TOPSIS method. It is applied to the case of Human Resources Manager selection in the seven regions into which the company divides its operation in Indonesia. Seven managers are selected; each from a set of five candidates. The criteria used are the measurement metrics developed by the HR department of the company. For the human judgment input in weighting these criteria, we asked five HR managers to give their opinions on the relative importance of the criteria. We also combine the opinions of these managers to gauge the overall employee perception of the company values, reflected in the criteria they think are more important.

The rest of this paper is organized as follows. This introduction is the first section of the main part of the paper, followed by a brief overview of related works in the Fuzzy AHP-TOPSIS method in the second section. The third section discussed the Fuzzy AHP-TOPSIS method itself. The fourth part is the implementation specifics of this work, and the results are presented and analyzed in the fifth section. The last section of this paper is the Conclusion, in which key takeaways from this paper are discussed, along with some suggestions for future work.