Combination of PIPRECIA and Multi-Attributive Ideal-Real Comparative Analysis for the Determination of Scholarship Students
Abstract
Scholarships are a form of financial assistance given to individuals to support their education. Criteria considered in the determination of scholarship recipients may include academic achievement, special talents, financial need, participation in extracurricular activities, and potential contributions to the community. The combination of weighting using PIPRECIA and MAIRCA can be a powerful approach in determining scholarship recipients. With PIPRECIA, scholarship providers can gather preferences from various relevant parties to determine the relative weight of each evaluation criterion. Furthermore, by applying MAIRCA, scholarship recipients can be evaluated based on these criteria by comparing between ideal attributes that reflect expected standards with real attributes that reflect the actual conditions of each recipient. By integrating these two methods, the process of determining scholarship recipients becomes more structured, transparent, and takes into account diverse preferences and priorities, ensuring that aid is distributed to the most deserving and needy individuals. The results of alternative rankings in determining scholarship recipients are 1st place with a final score of 0.071 obtained on behalf of Yusuf Maqdis, 2nd place with a final score of 0.068 obtained on behalf of Kurniawansyah, and 3rd place with a final score of 0.062 obtained on behalf of Ketut Purwanti.
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R. Trisudarmo, E. Sediyono, and J. E. Suseno, “Combination of Fuzzy C-Means Clustering Methods and Simple Additive Weighting in Scholarship of Decision Support Systems,” in 1st Annual International Conference on Natural and Social Science Education (ICNSSE 2020), 2021, pp. 161–169. doi: 10.2991/assehr.k.210430.025.
D. D. Stevens and M. M. Caskey, “Building a foundation for a successful doctoral student journey: A scholarship of teaching and learning investigation,” Innov. High. Educ., vol. 48, no. 3, pp. 433–455, 2023.
M. Marbun, M. Zarlis, and Z. Nasution, “Analysis of Application of the SAW, WP and TOPSIS Methods in Decision Support System Determining Scholarship Recipients at University,” in Journal of Physics: Conference Series, 2021, vol. 1830, no. 1, p. 12018.
H. Kurniawan, A. P. Swondo, E. P. Sari, K. Ummi, Yufrizal, and F. Agustin, “Decision Support System To Determine The Student Achievement Scholarship Recipients Using Fuzzy Multiple Attribute Decision Making (FMADM) With SAW,” in 2019 7th International Conference on Cyber and IT Service Management (CITSM), Nov. 2019, vol. 7, pp. 1–6. doi: 10.1109/CITSM47753.2019.8965326.
I. G. A. A. M. Aristamy, I. G. I. Sudipa, C. P. Yanti, I. Pratistha, and V. D. Waas, “An Application of a Decision Support System for Senior High School Scholarship with Modified MADM Method,” in 2021 6th International Conference on New Media Studies (CONMEDIA), Oct. 2021, pp. 54–59. doi: 10.1109/CONMEDIA53104.2021.9617180.
K. AKILLI and E. İPEKÇİ ÇETİN, “Selection of Scholarship Students in Higher Education with VIKOR Method,” Int. J. Assess. Tools Educ., vol. 7, no. 3, pp. 379–391, 2020, doi: https://doi.org/10.21449/ijate.684360.
A. Karthikeyan, A. Garg, P. K. Vinod, and U. D. Priyakumar, “Machine learning based clinical decision support system for early COVID-19 mortality prediction,” Front. public Heal., vol. 9, p. 626697, 2021.
S. R. Bonab, S. J. Ghoushchi, M. Deveci, and G. Haseli, “Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach,” Expert Syst. Appl., vol. 214, p. 119205, 2023.
J. Zhang, J. Liu, S. Hirdaris, M. Zhang, and W. Tian, “An interpretable knowledge-based decision support method for ship collision avoidance using AIS data,” Reliab. Eng. Syst. Saf., vol. 230, p. 108919, 2023.
I. M. Jiskani, Q. Cai, W. Zhou, X. Lu, and S. A. A. Shah, “An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining,” Expert Syst. Appl., vol. 188, p. 116062, 2022, doi: https://doi.org/10.1016/j.eswa.2021.116062.
M. Gheibi et al., “A sustainable decision support system for drinking water systems: Resiliency improvement against cyanide contamination,” Infrastructures, vol. 7, no. 7, p. 88, 2022.
B. Unhelkar, S. Joshi, M. Sharma, S. Prakash, A. K. Mani, and M. Prasad, “Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2, p. 100084, Nov. 2022, doi: 10.1016/j.jjimei.2022.100084.
J. S. Mboli, D. Thakker, and J. L. Mishra, “An Internet of Things‐enabled decision support system for circular economy business model,” Softw. Pract. Exp., vol. 52, no. 3, pp. 772–787, Mar. 2022, doi: 10.1002/spe.2825.
S. Chakraborty, P. Chatterjee, and P. P. Das, “Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) Method,” in Multi-Criteria Decision-Making Methods in Manufacturing Environments, Apple Academic Press, 2024, pp. 289–296.
I. M. Hezam, N. R. D. Vedala, B. R. Kumar, A. R. Mishra, and F. Cavallaro, “Assessment of Biofuel Industry Sustainability Factors Based on the Intuitionistic Fuzzy Symmetry Point of Criterion and Rank-Sum-Based MAIRCA Method,” Sustainability, vol. 15, no. 8, p. 6749, Apr. 2023, doi: 10.3390/su15086749.
B. Turanoğlu Şirin, “A hybrid approach based on fuzzy BWM and MAIRCA for selection of engine in rotary wing unmanned aerial vehicle design,” J. Intell. Fuzzy Syst., no. Preprint, pp. 1–12, 2023.
S. Qahtan et al., “Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model,” Appl. Soft Comput., vol. 138, p. 110170, 2023.
F. Ecer, A. Böyükaslan, and S. Hashemkhani Zolfani, “Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology,” Axioms, vol. 11, no. 8, p. 404, Aug. 2022, doi: 10.3390/axioms11080404.
D. Božanić, D. Pamučar, A. Milić, D. Marinković, and N. Komazec, “Modification of the logarithm methodology of additive weights (LMAW) by a triangular fuzzy number and its application in multi-criteria decision making,” Axioms, vol. 11, no. 3, p. 89, 2022.
S. Hadian, E. Shahiri Tabarestani, and Q. B. Pham, “Multi attributive ideal-real comparative analysis (MAIRCA) method for evaluating flood susceptibility in a temperate Mediterranean climate,” Hydrol. Sci. J., vol. 67, no. 3, pp. 401–418, Feb. 2022, doi: 10.1080/02626667.2022.2027949.
S. Riahi, A. Bahroudi, M. Abedi, and S. Aslani, “Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution,” Geochemistry, vol. 82, no. 3, p. 125898, Sep. 2022, doi: 10.1016/j.chemer.2022.125898.
S. Chatterjee and S. Chakraborty, “A multi-attributive ideal-real comparative analysis-based approach for piston material selection,” Opsearch, vol. 59, no. 1, pp. 207–228, 2022.
J. García Mestanza and R. Bakhat, “A fuzzy ahp-mairca model for overtourism assessment: The case of Malaga province,” Sustainability, vol. 13, no. 11, p. 6394, 2021.
A. Ulutaş, A. Topal, D. Karabasevic, D. Stanujkic, G. Popovic, and F. Smarandache, “Prioritization of logistics risks with plithogenic PIPRECIA method,” in International Conference on Intelligent and Fuzzy Systems, 2021, pp. 663–670.
M. Bakır, Ş. Akan, and E. Özdemir, “Regional aircraft selection with fuzzy PIPRECIA and fuzzy MARCOS: A case study of the Turkish airline industry,” Facta Univ. Ser. Mech. Eng., vol. 19, no. 3, pp. 423–445, 2021.
S. H. Hadad et al., “Student Ranking Based on Learning Assessment Using the Simplified PIPRECIA Method and CoCoSo Method,” J. Comput. Syst. Informatics, vol. 5, no. 1, 2023, doi: 10.47065/josyc.v5i1.4544.
I. Đalić, Ž. Stević, C. Karamasa, and A. Puška, “A novel integrated fuzzy PIPRECIA–interval rough SAW model: Green supplier selection,” Decis. Mak. Appl. Manag. Eng., vol. 3, no. 1, pp. 126–145, 2020.
A. Blagojević, S. Kasalica, Ž. Stević, G. Tričković, and V. Pavelkić, “Evaluation of safety degree at railway crossings in order to achieve sustainable traffic management: A novel integrated fuzzy MCDM model,” Sustainability, vol. 13, no. 2, p. 832, 2021.
H. B. Santoso, “Metode Pembobotan Simplified Pivot Pairwise Relative Criteria Importance Assessment dan COPRAS Dalam Penentuan Seleksi Penerimaan Guru,” J. Artif. Intell. Technol. Inf., vol. 1, no. 4, pp. 154–163, 2023, doi: 10.58602/jaiti.v1i4.84.
P. Rani, S.-M. Chen, and A. R. Mishra, “Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets,” Inf. Sci. (Ny)., vol. 644, p. 119274, Oct. 2023, doi: 10.1016/j.ins.2023.119274.
S. Boral, I. Howard, S. K. Chaturvedi, K. McKee, and V. N. A. Naikan, “An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA,” Eng. Fail. Anal., vol. 108, p. 104195, 2020.
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Copyright (c) 2024 Sitna Hajar Hadad, Iryanto Chandra, Sufiatul Maryana, Setiawansyah Setiawansyah

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