Integration of Data Assessment Method Weighting and Proximity Indexed Value for Best Employee Selection in Decision Support Systems

Setiawansyah Setiawansyah; Dyah Ayu Megawaty; Faruk Ulum; Agung Deni  Wahyudi; Fadila Shely  Amalia

doi:10.58602/chain.v4i4.378

Setiawansyah Setiawansyah ^{(Corresponding Author)} Universitas Teknokrat Indonesia
Dyah Ayu Megawaty Universitas Teknokrat Indonesia
Faruk Ulum Universitas Teknokrat Indonesia
Agung Deni Wahyudi Universitas Teknokrat Indonesia
Fadila Shely Amalia Universitas Teknokrat Indonesia

DOI: https://doi.org/10.58602/chain.v4i4.378

Keywords: Decision Support System, Best Employee Selection, Data Assessment Method, Proximity Indexed Value, Multi-Criteria Decision Making

Abstract

The selection of the best employee is a multi-criteria decision-making problem because employee performance is evaluated using several criteria with different levels of importance. Subjective criterion weighting may not fully represent the characteristics of the assessment data and can influence the resulting decision. This study proposes the integration of Data Assessment Method (DAM) Weighting and Proximity Indexed Value (PIV) within a Decision Support System for best employee selection. DAM Weighting is employed to determine objective criterion weights based on the information contained in the assessment data, while PIV is used to evaluate the relative proximity of each employee alternative to the reference condition. The results show that the criterion weights are relatively balanced, with R1 obtaining the highest weight of 0.1494 and R4 the lowest weight of 0.1381. The PIV calculation produces different proximity values among the nine employee alternatives, with Alt-08 obtaining the lowest value of -2.2864, followed by Alt-09 at -1.8580 and Alt-06 at -1.7148. Based on the resulting ranking, Alt-08 is identified as the best employee. These findings indicate that the integration of DAM Weighting and PIV can provide an objective, quantitative, and data-oriented mechanism for supporting best employee selection in a Decision Support System.

References

S. Dündar, “Prioritizing the Regional Preferences of Turkish Investors Regarding Foreign Direct Investment by ARLON Method,” Konya J. Eng. Sci., vol. 13, no. 3, pp. 927–946, 2025, doi: 10.36306/konjes.1648279.

G. Demir, D. Pamucar, and P. Chatterjee, “An Integrated Fuzzy Multi-Criteria Framework for Evaluation of Sustainable Communication Technologies,” Spectr. Oper. Res., vol. 2, no. 1 SE-Articles, pp. 326–345, Feb. 2025, doi: 10.31181/sor21202529.

V. Simic, A. Ebadi Torkayesh, and A. Ijadi Maghsoodi, “Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm,” Ann. Oper. Res., vol. 328, no. 1, pp. 1105–1150, 2023, doi: 10.1007/s10479-022-04822-0.

P. Zofaisal Hamid and H. Sulistiani, “Kombinasi Metode Pembobotan Entropy dan Multi-Attribute Utility Theory Dalam Penentuan Karyawan Terbaik,” JUSTINDO, vol. 9, no. 2, pp. 121–132, Aug. 2024, doi: 10.32528/justindo.v9i2.1963.

N. P. Siburian and L. Sahriani, “Sistem Pendukung Keputusan Pemilihan Pegawai Honorer Kelurahan Menerapkan Metode MOORA,” TIN Terap. Inform. Nusant., vol. 3, no. 10, pp. 395–404, Mar. 2023, doi: 10.47065/tin.v3i10.4148.

J. Liu, B. Gu, and J. Chen, “Enablers for maritime supply chain resilience during pandemic: An integrated MCDM approach,” Transp. Res. Part A Policy Pract., vol. 175, p. 103777, 2023, doi: https://doi.org/10.1016/j.tra.2023.103777.

M. A. M. Al-Gerafi et al., “Promoting inclusivity in education amid the post-COVID-19 challenges: An interval-valued fuzzy model for pedagogy method selection,” Int. J. Manag. Educ., vol. 22, no. 3, p. 101018, 2024, doi: https://doi.org/10.1016/j.ijme.2024.101018.

J. Jin, L. Zou, Z. Long, T. Shi, and Y. Chang, “A Parameterized Intuitionistic Fuzzy Distance Measure-Based CoCoSo Method and Its Application in Stock Selection,” Mathematics, vol. 14, no. 5. p. 810, 2026. doi: 10.3390/math14050810.

P. Rani, A. R. Mishra, E. B. Tirkolaee, A. M. Alshamrani, and A. F. Alrasheedi, “Pythagorean fuzzy comprehensive distance-based ranking approach for assessing industry 4.0 adoption strategies in the automotive manufacturing sector,” Adv. Eng. Informatics, vol. 65, p. 103359, 2025, doi: https://doi.org/10.1016/j.aei.2025.103359.

G. C. Yalçın, K. Kara, E. K. Özekenci, V. Simic, and D. Pamucar, “Promoting biodiversity-based trade: A fuzzy multi-criteria decision analytics for performance measurement of sustainable development in megadiverse countries,” J. Clean. Prod., vol. 547, p. 147831, 2026, doi: https://doi.org/10.1016/j.jclepro.2026.147831.

T. T. Worku, “Formwork material selection and optimization by a comprehensive integrated subjective–objective criteria weighting MCDM model,” Discov. Mater., vol. 5, no. 1, p. 2, 2025, doi: 10.1007/s43939-024-00162-x.

D. A. Megawaty et al., “DAM Weighting: A New Approach to Determining Criteria Weighting in Multi-Criteria Decision Making,” Evergreen, vol. 13, no. 2, pp. 788–800, 2026, doi: 10.5109/7432655.

D. D. Trung, N. T. P. Giang, D. Van Duc, T. Van Dua, and H. X. Thinh, “The Use of SAW, RAM and PIV Decision Methods in Determining the Optimal Choice of Materials for the Manufacture of Screw Gearbox Acceleration Boxes,” Int. J. Mech. Eng. Robot. Res., vol. 13, no. 3, pp. 338–347, 2024, doi: 10.18178/ijmerr.13.3.338-347.

N. Ersoy and S. Taslak, “Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector,” Ege Acad. Rev., vol. 23, no. 3, pp. 341–362, 2023, doi: 10.21121/eab.986122.

D. T. Do, “Assessing the Impact of Criterion Weights on the Ranking of the Top Ten Universities in Vietnam,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 4 SE-, pp. 14899–14903, Aug. 2024, doi: 10.48084/etasr.7607.