WASPAS Implementation on Digital Talent Scholarship Selection

 Muhammad Dahria (STMIK Triguna Dharma, Medan, Indonesia)
 (*)Saiful Nur Arif Mail (STMIK Triguna Dharma, Medan, Indonesia)
 Badrul Anwar (STMIK Triguna Dharma, Medan, Indonesia)

(*) Corresponding Author

Submitted: November 16, 2022; Published: November 30, 2022

Abstract

Digital transformation also greatly influences the direction of economic development in Indonesia. This has an impact on the increasing need for IT experts, especially in the programming field. However, the number of skilled and reliable human resource programmers in Indonesia is still very small and has not been able to meet the demands of the digital industry. Therefore, information technology and decision support systems are needed as a tool to determine the selection of qualified junior mobile programming talent. As in previous research, a decision support system is a system specifically designed for the decision-making process on semi-structured and unstructured problems. In order for the purpose of the decision support system to be realized properly, it is assisted by using one of the methods in the decision support system, namely the WASPAS method which is a combination of the WSM and WPM methods. This method can be used to solve MCDM (Multi Criteria Decision Making) problems

Keywords


Decission Support System; WASPAS; Digital Tallent

Full Text:

PDF


Article Metrics

Abstract view : 349 times
PDF - 165 times

References

M. Deveci, F. Canıtez, and I. Gökaşar, “WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station,” Sustain. Cities Soc., vol. 41, pp. 777–791, Aug. 2018, doi: 10.1016/j.scs.2018.05.034.

S. Bid and G. Siddique, “Human risk assessment of Panchet Dam in India using TOPSIS and WASPAS Multi-Criteria Decision-Making (MCDM) methods,” Heliyon, vol. 5, no. 6, p. e01956, Jun. 2019, doi: 10.1016/j.heliyon.2019.e01956.

M. S. Ramadhan et al., “Analysis of FAM in satisfaction of inpatient services,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 5, pp. 1529–1534, 2021, doi: 10.12928/TELKOMNIKA.v19i5.20295.

M. P. Fanti, G. Iacobellis, M. Nolich, A. Rusich, and W. Ukovich, “A Decision Support System for Cooperative Logistics,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 2, pp. 732–744, Apr. 2017, doi: 10.1109/TASE.2017.2649103.

B. Malmir, M. Amini, and S. I. Chang, “A medical decision support system for disease diagnosis under uncertainty,” Expert Syst. Appl., vol. 88, pp. 95–108, Dec. 2017, doi: 10.1016/j.eswa.2017.06.031.

N. Sahebjamnia, S. A. Torabi, and S. A. Mansouri, “A hybrid decision support system for managing humanitarian relief chains,” Decis. Support Syst., vol. 95, no. November, pp. 12–26, 2017, doi: 10.1016/j.dss.2016.11.006.

A. Yanie et al., “Web Based Application for Decision Support System with ELECTRE Method,” in Journal of Physics: Conference Series, 2018, vol. 1028, no. 1, p. 012054, doi: 10.1088/1742-6596/1028/1/012054.

A. H. Nasyuha, Zulham, I. J. Cik, M. Amin, S. C. Setia, and D. Siregar, Journal of Physics: Conference Series: Preface. 2019.

D. Handoko, M. Mesran, S. D. Nasution, Y. Yuhandri, and H. Nurdiyanto, “Application Of Weight Sum Model (WSM) In Determining Special Allocation Funds Recipients,” IJICS (International J. Informatics Comput. Sci., vol. 1, no. 2, pp. 31–35, 2017.

B. Malmir, M. Amini, and S. I. Chang, “A medical decision support system for disease diagnosis under uncertainty,” Expert Syst. Appl., vol. 88, pp. 95–108, Dec. 2017, doi: 10.1016/j.eswa.2017.06.031.

L. Wang, H. Zhang, J. Wang, and L. Li, “Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project,” Appl. Soft Comput., vol. 64, pp. 216–226, Mar. 2018, doi: 10.1016/j.asoc.2017.12.014.

L. T. Sianturi, “Implementation of Weight Sum Model ( WSM ) in the Selection of Football Athletes,” Int. J. Informatics Comput. Sci. (The IJICS), vol. 3, no. 1, pp. 24–27, 2019.

M. Ikram, Q. Zhang, and R. Sroufe, “Developing integrated management systems using an AHP‐Fuzzy VIKOR approach,” Bus. Strateg. Environ., vol. 29, no. 6, pp. 2265–2283, Sep. 2020, doi: 10.1002/bse.2501.

D. Siregar et al., “Multi-Attribute Decision Making with VIKOR Method for Any Purpose Decision,” J. Phys. Conf. Ser., vol. 1019, no. 1, 2018, doi: 10.1088/1742-6596/1019/1/012034.

A. Mardani et al., “A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments,” Appl. Soft Comput., vol. 57, pp. 265–292, Aug. 2017, doi: 10.1016/j.asoc.2017.03.045.

S. Agarwal, R. Kant, and R. Shankar, “Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach,” Int. J. Disaster Risk Reduct., vol. 51, p. 101838, Dec. 2020, doi: 10.1016/j.ijdrr.2020.101838.

H. Prajapati, R. Kant, and R. Shankar, “Prioritizing the solutions of reverse logistics implementation to mitigate its barriers: A hybrid modified SWARA and WASPAS approach,” J. Clean. Prod., vol. 240, p. 118219, Dec. 2019, doi: 10.1016/j.jclepro.2019.118219.

Z. Turskis, N. Goranin, A. Nurusheva, and S. Boranbayev, “A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development,” Sustain., vol. 11, no. 2, 2019, doi: 10.3390/su11020424.

K. Rudnik, G. Bocewicz, A. Kucińska-Landwójtowicz, and I. D. Czabak-Górska, “Ordered fuzzy WASPAS method for selection of improvement projects,” Expert Syst. Appl., vol. 169, p. 114471, May 2021, doi: 10.1016/j.eswa.2020.114471.

M. Badalpur and E. Nurbakhsh, “An application of WASPAS method in risk qualitative analysis: a case study of a road construction project in Iran,” Int. J. Constr. Manag., vol. 21, no. 9, pp. 910–918, Sep. 2021, doi: 10.1080/15623599.2019.1595354.

J. Ali, Z. Bashir, and T. Rashid, “WASPAS-based decision making methodology with unknown weight information under uncertain evaluations,” Expert Syst. Appl., vol. 168, p. 114143, Apr. 2021, doi: 10.1016/j.eswa.2020.114143.

D. Pamucar, M. Deveci, F. Canıtez, and V. Lukovac, “Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model,” Eng. Appl. Artif. Intell., vol. 93, p. 103703, Aug. 2020, doi: 10.1016/j.engappai.2020.103703.

G. Stojić, Ž. Stević, J. Antuchevičienė, D. Pamučar, and M. Vasiljević, “A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products,” Information, vol. 9, no. 5, p. 121, May 2018, doi: 10.3390/info9050121.

A. R. Mishra, P. Rani, K. R. Pardasani, and A. Mardani, “A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures,” J. Clean. Prod., vol. 238, no. 3, p. 117901, Nov. 2019, doi: 10.1016/j.jclepro.2019.117901.

M. Keshavarz-Ghorabaee, M. Amiri, M. Hashemi-Tabatabaei, E. K. Zavadskas, and A. Kaklauskas, “A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation,” Mathematics, vol. 8, no. 12, p. 2202, Dec. 2020, doi: 10.3390/math8122202.

O. Gireesha, N. Somu, K. Krithivasan, and S. S. V.S., “IIVIFS-WASPAS: An integrated Multi-Criteria Decision-Making perspective for cloud service provider selection,” Futur. Gener. Comput. Syst., vol. 103, pp. 91–110, Feb. 2020, doi: 10.1016/j.future.2019.09.053.

K. A. Alam, R. Ahmed, F. S. Butt, S.-G. Kim, and K.-M. Ko, “An Uncertainty-aware Integrated Fuzzy AHP-WASPAS Model to Evaluate Public Cloud Computing Services,” Procedia Comput. Sci., vol. 130, pp. 504–509, 2018, doi: 10.1016/j.procs.2018.04.068.

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Muhammad Dahria, Saiful Nur Arif, Badrul Anwar

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


The IJICS (International Journal of Informatics and Computer Science)
Published by STMIK Budi Darma.
Jl. Sisingamangaraja No.338 Simpang Limun, Medan, North Sumatera
Email: ijics.stmikbudidarma@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.