Developing a Decision Support System with Dynamic Criteria for The Best Employee Assessment

Deborah Kurniawati, Dara Kusumawati, Munifatul Arifah

Abstract


One of the efforts of the organizations management to foster the morale of human resources (HR) is to reward the best performing HR. HR with the best performance is assessed by various criteria determined by the organization. The problem is, how can a large organization that has many branches and / or organizational fields be able to select HR with the best performance objectively; while each branch or field of organization can have different emphases or interests in each HR assessment criteria. This research develops a decision support system that can be used to select the best HR with dynamic criteria and weighting. Criteria can be added or reduced, also the weight of the criteria can be adjusted to the system user. Decision support system was developed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. With TOPSIS it is possible to enter criteria that are expected to be positive and criteria that are expected to be negative. The results of the research conducted are a decision support system for determining the best employees with a dynamic and flexible multi model, where the criteria and weighting can be adjusted to the needs of the branch office or each part.

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References


Zaouga W, Rabai L. B. A., Alalyan WR., 2019, Towards an Ontology Based-Approach for Human Resource Management, The 10th International Conference on Ambient Systems, Networks and Technologies (ANT), April 29 – May 2, 2019, Leuven, Belgium, Procedia Computer Science 151 (2019) 417–424.

Motilewa B. D., Bisi-Adeniyi C. O., Fambegbe O. A, Oyeyemi A.I., Worlu R. E. K., Moses C. L., 2018, Survey data on employees’ development and employees’ satisfaction in oil and gas firms in Nigeria, Data in Brief 19 (2018) 1816–1821.

Prayitno, E., Lukman, A., 2016, Peminatan Jurusan SMA Menggunakan Learning Vector Quantization, Seminar Riset Teknologi Informasi (SRITI), Juli 30, 2016, Yogyakarta, Indonesia, 204-212.

Yoon, K., 1980. Systems Selection by Multiple Attributes Decision Making. PhD Dissertation. Kansas State University, Manhattan, Kansas.

Hwang, C.L., Yoon, K.S., 1981. Multiple Attribute Decision-Making: Methods and Applications. Springer, New York. NY, USA.

Srikrishna, S., Sreenivasula, R.A., Vani, S., 2014. A new car selection in the market using TOPSIS technique. Int. J. Eng. Res. Gen. Sci. 2, 4177–4181.

Bid S., Siddique G., 2019, Human risk assessment of Panchet Dam in India using TOPSIS and WASPAS Multi-Criteria Decision-Making (MCDM) methods, Heliyon 5 e01956

Lai, Y.J., Liu, T.Y., Hwang, C.L., 1994, TOPSIS for MODM. Eur. J. Oper. Res. 76, 486-500.

Dong, Q., Ai, X., Cao, G., Zhang, Y., Wang, X., 2010. Study on risk assessment of water security of drought periods based on entropy weight methods. Kybernetes 39, 864–870.

Yari, G., Chaji, A.R., 2012. Maximum Bayesian entropy method for determining ordered weighted averaging operator weights. Comput. Ind. Eng. 63, 338–342.

Baecher, G.B., 2016. Uncertainty in dam safety risk analysis. Georisk Assess. Manag. Risk Eng. Syst.

Geohazards 10, 92–108.

Yang, M.S., Nataliani, Y., 2017. A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy, 1. IEEE T. Fuzzy Syst.




DOI: https://doi.org/10.32535/jicp.v2i2.603

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Kuwait 

Kuwait University
Prof Majdi Anwar Quatinah (Advisor)
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ISSN 2622-0989 (Print)
ISSN: 2621-993X  (Online)

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