Combination of Criteria Importance Through Intercriteria Dependence and Simple Additive Weighting Methods for Multi-Criteria Barista Selection: A Decision Support Approach

Authors

  • Verra Sofica * (Corresponding Author) Universitas Bina Sarana Informatika image/svg+xml Author 1
    • Writing – Review & Editing
    • Writing – Original Draft Preparation
    • Validation
    • Software
    • Methodology
    • Conceptualization
  • Titik Misriati Universitas Bina Sarana Informatika image/svg+xml Author 2
    • Writing – Original Draft Preparation
    • Formal Analysis
    • Data Curation
    • Conceptualization
Keywords :
Decision Support System, Barista Selection, CRITID, SAW, Sensitivity Analysis
Abstract

Barista selection is an important process in the coffee shop and hospitality industry because baristas are required to possess not only technical skills but also coffee knowledge, communication, work speed, accuracy, and creativity. Evaluating candidates based on a single criterion can result in subjective and less representative decisions. Therefore, this study aims to develop a Decision Support System (DSS) for barista candidate selection by integrating the Criteria Importance Through Intercriteria Dependence (CRITID) and Simple Additive Weighting (SAW) methods. CRITID is applied to determine objective criterion weights by considering data variation and inter-criteria relationships, while SAW is used to calculate preference values and rank the candidates. The results show that Work Speed (CB-06) has the highest criterion weight of 0.1311, followed by Technical Skills (CB-02) with 0.1275 and Coffee Knowledge (CB-04) with 0.1253. The SAW ranking identifies A7 as the highest-ranked candidate with a preference value of 0.9682, followed by A3 with 0.9603 and A8 with 0.8994. Sensitivity analysis involving 32 scenarios with criterion weight changes of ±0.05 and ±0.10 indicates that the ranking structure is relatively stable, with A7 and A3 consistently maintaining the first and second positions. These findings demonstrate that the integration of CRITID and SAW can support a more objective, systematic, measurable, and stable decision-making process for barista candidate selection.

Downloads

Download data is not yet available.

References

[1] M. W. Arshad, R. R. Suryono, Y. Rahmanto, S. Sumanto, S. Sintaro, and S. Setiawansyah, “Combination of Objective Weighting Method using MEREC and A New Additive Ratio Assessment in Coffee Barista Admissions,” TIN Terap. Inform. Nusant., vol. 5, no. 3, pp. 220–231, Aug. 2024, doi: 10.47065/tin.v5i3.5771.

[2] S. Sintaro, “Sistem Pendukung Keputusan Penentuan Barista Terbaik Menggunakan Rank Sum dan Additive Ratio Assessment (ARAS),” J. Ilm. Comput. Sci., vol. 2, no. 1, pp. 39–49, 2023, doi: 10.58602/jics.v2i1.15.

[3] J. Qian, G. Zhou, W. He, Y. Cui, and H. Deng, “Optimization of teacher evaluation indicator system based on fuzzy-DEMATEL-BP,” Heliyon, vol. 10, no. 13, pp. 1–16, Jul. 2024, doi: 10.1016/j.heliyon.2024.e34034.

[4] M. Baydaş, M. Yılmaz, Ž. Jović, Ž. Stević, S. E. G. Özuyar, and A. Özçil, “A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches,” Financ. Innov., vol. 10, no. 1, p. 105, Mar. 2024, doi: 10.1186/s40854-023-00588-x.

[5] 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.

[6] A. Aytekin, “DETERMINING CRITERIA WEIGHTS FOR VEHICLE TRACKING SYSTEM SELECTION USING PIPRECIA-S,” J. Process Manag. new Technol., vol. 10, no. 1–2, pp. 115–124, Jun. 2022, doi: 10.5937/jpmnt10-38145.

[7] Q. Ma and H. Li, “A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning,” Expert Syst. Appl., vol. 242, p. 122746, May 2024, doi: 10.1016/j.eswa.2023.122746.

[8] N. Hendrastuty, M. G. Ar’nars, Setiawansyah, Mesran, T. A. Putra, and M. W. Arshad, “Decision Support System in Teacher Pedagogy Assessment Using MAIRCA with Geometric Mean Weighting,” in 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2024, pp. 265–270. doi: 10.1109/ICIMCIS63449.2024.10957630.

[9] A. Yudhistira, S. Setiawansyah, T. Ardiansah, S. Maryana, Y. Yadin, and R. Oktaviani, “Development of Multi-Attribute Utility Theory Methods in Dynamic Decision Models Using Change-Data Driven,” Evergreen, vol. 11, no. 4, pp. 3279–3289, Dec. 2024, doi: 10.5109/7326962.

[10] F. Tamtam and A. Tourabi, “Scenario-Based Security and Reliability Evaluation of Connected Autonomous Vehicles Using a Hybrid CRITID–Fuzzy BWM–VIKOR Swarm Approach,” Int. J. Inf. Eng. Electron. Bus., vol. 18, no. 3, p. 124, 2026, doi: 10.5815/ijieeb.2026.03.08.

[11] Q. Zhang, J. Fan, and C. Gao, “CRITID: enhancing CRITIC with advanced independence testing for robust multi-criteria decision-making,” Sci. Rep., vol. 14, no. 1, p. 25094, 2024, doi: 10.1038/s41598-024-75992-z.

[12] 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.

[13] Y. P. Suprapto, H. Haerudin, and A. Danuwidodo, “Decision Support System for Employee Performance Assessment Using Analytical Hierarchy Process and Simple Additive Weighting Methods,” J. Inf. Syst. Informatics, vol. 6, no. 2, pp. 766–780, 2024, doi: 10.51519/journalisi.v6i2.721.

[14] M. Soltani, B. Barekatain, F. Hendessi, and Z. Beheshti, “PSSN: a novel cache placement method based on adapted Shannon entropy and simple additive weighting method in named data networking,” Knowl. Inf. Syst., vol. 67, no. 2, pp. 1507–1540, 2025, doi: 10.1007/s10115-024-02266-5.

[15] 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: 10.1016/j.ijme.2024.101018.

[16] C. Z. Radulescu and M. Radulescu, “A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems,” Electronics, vol. 13, no. 4, p. 789, Feb. 2024, doi: 10.3390/electronics13040789.

[17] H. Sulistiani, S. 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.

[18] A. D. Putra, A. T. Priandika, D. Alita, C. Mario, A. D. Wahyudi, and Setiawansyah, “Implementations of the Entropy and Complex Proportional Assessment Methods in Determining the Best Independent Student Exchange,” in 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2024, pp. 247–252. doi: 10.1109/ICIMCIS63449.2024.10957397.

[19] G. Patel, S. Das, and R. Das, “Evaluation of optimal normalization techniques in multi-criteria decision-making to rank CMIP6 climate models,” Theor. Appl. Climatol., vol. 156, no. 7, p. 385, 2025, doi: 10.1007/s00704-025-05617-6.

[20] S. Eti et al., “Enhancing solar panel recycling efficiency through zero-shot learning and hybrid fuzzy decision-making techniques,” Renew. Energy, vol. 256, p. 124411, 2026, doi: 10.1016/j.renene.2025.124411.

View Article:

Downloads

Online First Article:
01-09-2026
Article Publication Date:
20-11-2026

How to Cite

Sofica, V., & Misriati, T. (2026). Combination of Criteria Importance Through Intercriteria Dependence and Simple Additive Weighting Methods for Multi-Criteria Barista Selection: A Decision Support Approach. Journal of Decision Support Systems and Multi-Criteria Decision Making, 1(1), 43-62. https://doi.org/10.67449/jodesma.v1i1.3