IT Personnel Recruitment Decision Support System: Combination of TOPSIS and Entropy Weighting Methods

Setiawansyah Setiawansyah

doi:10.58602/jaiti.v2i3.131

Setiawansyah Setiawansyah Universitas Teknokrat Indonesia

DOI: https://doi.org/10.58602/jaiti.v2i3.131

Keywords: Candidate, Decision Support System, Entropy, Recruitment, TOPSIS

Abstract

IT personnel recruitment is an important process that aims to get the best talent in the field of information technology to support operations and innovation in an organization. IT personnel recruitment faces several key challenges that often make it difficult for companies to find the right candidates. One of the main problems is that the recruitment process can also be constrained by difficulties in assessing a candidate's cultural and interpersonal fit, where high technical skills are not necessarily balanced by good communication and teamwork skills. The purpose of this study is to apply DSS that integrates the TOPSIS and entropy weighting methods in the IT recruitment process, so that it can help companies select the best candidates effectively and objectively. The system is designed to improve the accuracy of candidate identification through multi-criteria analysis. An IT personnel recruitment decision support system that combines TOPSIS and entropy methods is an innovative approach designed to increase effectiveness in selecting the best candidates based on relevant criteria. The results of Candidate G ranking were ranked highest with a score of 0.932, followed by Candidate A with a score of 0.7069, Candidate C with a score of 0.645, and Candidate E with a score of 0.6443. Furthermore, Candidate I in the middle position with a score of 0.5023, followed by Candidate D with a score of 0.3417. Candidate B and Candidate H are in a lower position with values of 0.2188 and 0.1817, respectively. Candidate F ranked at the bottom with a score of 0.0519.

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References

S. I. Ali et al., “Risk quantification and ranking of oil fields and wells facing asphaltene deposition problem using fuzzy TOPSIS coupled with AHP,” Ain Shams Eng. J., vol. 15, no. 1, p. 102289, 2024, doi: https://doi.org/10.1016/j.asej.2023.102289.

D. Tešić, M. Radovanović, D. Božanić, D. Pamucar, A. Milić, and A. Puška, “Modification of the DIBR and MABAC Methods by Applying Rough Numbers and Its Application in Making Decisions,” Information, vol. 13, no. 8, p. 353, Jul. 2022, doi: 10.3390/info13080353.

A. R. Mishra, P. Rani, F. Cavallaro, I. M. Hezam, and J. Lakshmi, “An Integrated Intuitionistic Fuzzy Closeness Coefficient-Based OCRA Method for Sustainable Urban Transportation Options Selection,” Axioms, vol. 12, no. 2, p. 144, Jan. 2023, doi: 10.3390/axioms12020144.

C. Meske and E. Bunde, “Design principles for user interfaces in AI-Based decision support systems: The case of explainable hate speech detection,” Inf. Syst. Front., vol. 25, no. 2, pp. 743–773, 2023.

M. Deveci, D. Pamucar, I. Gokasar, M. Köppen, B. B. Gupta, and T. Daim, “Evaluation of Metaverse traffic safety implementations using fuzzy Einstein based logarithmic methodology of additive weights and TOPSIS method,” Technol. Forecast. Soc. Change, vol. 194, p. 122681, Sep. 2023, doi: 10.1016/j.techfore.2023.122681.

H. Sulistiani, S. Setiawansyah, A. F. O. Pasaribu, P. Palupiningsih, K. Anwar, and V. H. Saputra, “New TOPSIS: Modification of the TOPSIS Method for Objective Determination of Weighting,” Int. J. Intell. Eng. Syst., vol. 17, no. 5, pp. 991–1003, Oct. 2024, doi: 10.22266/ijies2024.1031.74.

Setiawansyah, A. A. Aldino, P. Palupiningsih, G. F. Laxmi, E. D. Mega, and I. Septiana, “Determining Best Graduates Using TOPSIS with Surrogate Weighting Procedures Approach,” in 2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT), 2023, pp. 60–64. doi: 10.1109/IConNECT56593.2023.10327119.

S. Setiawansyah, V. H. Saputra, S. Sintaro, and A. A. Aldino, “Multiple Attribute Decision Making Menggunakan Metode TOPSIS Dalam Penentuan Staff Marketing Terbaik,” Bull. Artif. Intell., vol. 2, no. 2, pp. 127–136, 2023.

D. D. Trung, “Application of TOPSIS and PIV methods for multi-criteria decision making in hard turning process,” J. Mach. Eng., vol. 21, no. 4, pp. 57–71, 2021.

T. Van Dua, D. Van Duc, N. C. Bao, and D. D. Trung, “Integration of objective weighting methods for criteria and MCDM methods: application in material selection,” EUREKA Phys. Eng., no. 2, pp. 131–148, Mar. 2024, doi: 10.21303/2461-4262.2024.003171.

I. Mukhametzyanov, “Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD,” Decis. Mak. Appl. Manag. Eng., vol. 4, no. 2, pp. 76–105, Oct. 2021, doi: 10.31181/dmame210402076i.

M. P. Libório, R. Karagiannis, A. M. A. Diniz, P. I. Ekel, D. A. G. Vieira, and L. C. Ribeiro, “The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators,” Entropy, vol. 26, no. 2, p. 143, Feb. 2024, doi: 10.3390/e26020143.

M. W. Arshad, D. Darwis, H. Sulistiani, R. R. Suryono, Y. Rahmanto, and D. A. Megawaty, “Combination of Weighted Product Method and Entropy Weighting in the Best Warehouse Employee Recommendation,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 5, no. 1, pp. 193–202, 2024, doi: 10.30865/klik.v5i1.2095.

A. D. Wahyudi, S. Sumanto, S. Setiawansyah, and A. Yudhistira, “Sistem Pendukung Keputusan Rekomendasi Hotel Bintang Tiga Menggunakan Kombinasi Entropy dan Combine Compromise Solution,” Bull. Artif. Intell., vol. 3, no. 1, pp. 16–25, Apr. 2024, doi: 10.62866/buai.v3i1.142.

I. Adhicandra, J. Hutahaean, R. R. Ismail, N. Mulyani, and N. Hasti, “Seleksi Staff IT Menggunakan Metode Preference Selection Index (PSI),” Bull. Inf. Technol., vol. 5, no. 2, pp. 125–135, 2024, doi: 10.47065/bit.v5i2.1390.

Murnawan, S. Lestari, and R. Samihardjo, “Penerapan Metode F-AHP dan F-TOPSIS Dalam Proses Seleksi Karyawan Untuk Bidang Teknologi Informasi,” Teknika, vol. 13, no. 1, pp. 35–44, Jan. 2024, doi: 10.34148/teknika.v13i1.688.

H. Sulistiani, Setiawansyah, P. Palupiningsih, F. Hamidy, P. L. Sari, and Y. Khairunnisa, “Employee Performance Evaluation Using Multi-Attribute Utility Theory (MAUT) with PIPRECIA-S Weighting: A Case Study in Education Institution,” in 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS), 2023, pp. 369–373. doi: 10.1109/ICIMCIS60089.2023.10349017.