NON-CASH FOOD ASSISTANCE PROGRAM BENEFICIARIES BASED ON COPRAS AND CODAS

  • Dwi Marisa Midyanti (1*) Universitas Tanjungpura
  • Syamsul Bahri (2) Universitas Tanjungpura
  • Hafizhah Insani Midyanti (3) Universitas Pendidikan Indonesia

  • (*) Corresponding Author
Keywords: BPNT, MCDM, COPRAS, CODAS, Spearman Rank Correlation

Abstract

Determination of recipients of the Non-Cash Food Assistance Program (BPNT) is a matter that causes problems if it is not carried out in an objective, transparent, and targeted manner. Previous studies on BPNT were based on a specific method, which did not use a negative trend in the criteria. In this study, the Multi-Criteria Decision Making (MCDM) approach was used to recommend the recipients of the BPNT program. Two MCDM models were used in this study, COPRAS and CODAS methods. Spearman's rank correlation method was used to determine the best model and measure the degree of similarity between the results obtained from different models. Spearman rank correlation shows that COPRAS and CODAS have a strong positive correlation of 0.89899. The combined COPRAS-CODAS ranking model produces a very strong positive correlation value of 0.9744 for both methods, so the model is used for recommendations for BPNT program recipients.

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References

E. Y. Yunus, “Implementasi Program Bantuan Pangan Non Tunai (Bpnt) Di Kecamatan Kanigaran Kota Probolinggo,” Reformasi, vol. 9, no. 2, pp. 138–152, 2019, doi: 10.33366/rfr.v9i2.1454.

I. Fadlurrohim, S. A. Nulhaqim, and S. Sulastri, “Implementasi Program Bantuan Pangan Non Tunai (Studi Kasus Di Kota Cimahi),” Share Soc. Work J., vol. 9, no. 2, pp. 122–129, 2020, doi: 10.24198/share.v9i2.20326.

S. F. Azzahra, Implementasi Metode Composite Performance Index (CPI) Dalam Penentuan Calon Penerima Bantuan Pangan Non Tunai (BPNT) (Studi Kasus : Kelurahan Tambelan Sampit), Skripsi, Pontianak : Universitas Tanjungpura, 2022.

M. Keshavarz-Ghorabaee, M. Amiri, E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, “Simultaneous Evaluation of Criteria and Alternatives (SECA) for Multi-Criteria Decision-Making,” Informatica, vol. 29, no. 2, pp. 265–280, 2018, doi: 10.15388/Informatica.2018.167.

N. Ersoy, “Comparative Analysis of MCDM Methods for The Assessment of ICT Development in G7 Contries,” KAÜİİBFD, vol. 13, no. 25, pp. 55–73, 2022, doi: 10.1016/j.omega.2015.05.013.

B. Ghosh and S. Mukhopadhyay, “Erosion Susceptibility Mapping of Sub-Watersheds for Management Prioritization using MCDM-based Ensemble Approach,” Arab. J. Geosci., vol. 14, no. 36, pp. 1–18, 2021, doi: 10.1007/s12517-020-06297-4.

N. A. Osintsev, “Multi-Criteria Decision-Making Methods in Green Logistics,” World Transp. Transp., vol. 19, no. 5(96), pp. 231–240, 2021, doi: 10.30932/1992-3252-2021-19-5-13.

Y. Kustiyahningsih, Husni, and I. Q. Aini, “Integration of FAHP and COPRAS Method for New Student Admission Decision Making,” in 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), 2020, no. November, pp. 1–6, doi: 10.1109/ICVEE50212.2020.9243260.

A. Patel, S. Jha, R. Soni, and K. Fuse, “Notice of Removal: Comparative study of MCDM Techniques COPRAS and TOPSIS for selection of Electric Motorcycles,” in 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), 2020, pp. 54–59, doi: 10.1109/ICIEA49774.2020.9101932.

H. S. Dhiman and D. Deb, “Fuzzy TOPSIS and fuzzy COPRAS based Multi-Criteria Decision Making for Hybrid Wind Farms,” Energy, vol. 202, pp. 1–10, 2020, doi: 10.1016/j.energy.2020.117755.

R. P. Setyono and R. Sarno, “Comparative Method of MOORA and COPRAS Based on Weighting of the Best Worst Method in Supplier Selection at ABC Mining Companies in Indonesia,” in International Conference on Information and Communications Technology (ICOIACT), 2019, pp. 354–359, doi: 10.1109/ICOIACT46704.2019.8938520.

A. S. Ajrina, R. Sarno, and R. V. H. Ginardi, “Comparison of moora and copras methods based on geographic information system for determining potential zone of pasir batu mining,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019, pp. 360–365, doi: 10.1109/ICOIACT46704.2019.8938465.

M. Keshavarz Ghorabaee, E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, “A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 50, no. 3, pp. 25–44, 2016.

M. Regaieg and H. M. Frikha, “Inferring Criteria Weight Parameters in CODAS method,” Int. J. Multicriteria Decis. Mak., vol. X, no. Y, pp. 1–19, 2021, doi: 10.1504/ijmcdm.2021.10044853.

E. M. Abdelkader, A. Al-Sakkaf, and G. Alfalah, “Optimizing Material Selection using a Hybridized Multi-Attribute Decision Making Model,” WSEAS Trans. Syst. Control, vol. 16, pp. 404–421, 2021, doi: 10.37394/23203.2021.16.36.

L. Thi Diem My, C.-N. Wang, and N. Van Thanh, “Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain,” Comput. Mater. Contin., vol. 73, no. 2, pp. 4003–4015, 2022, doi: 10.32604/cmc.2022.030209.

V. Sivalingam, P. G. Kumar, R. Prabakaran, J. Sun, R. Velraj, and S. C. Kim, “An Automotive Radiator With Multi-Walled Carbon-based Nanofluids: A study on Heat Transfer Optimization using MCDM Techniques,” Case Stud. Therm. Eng., vol. 29, pp. 1–17, 2022, doi: 10.1016/j.csite.2021.101724.

K. Gupta et al., “Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP,” Appl. Sci., vol. 12, no. 4156, pp. 1–26, 2022, doi: 10.3390/app12094156.

Published
2023-02-27
How to Cite
[1]
D. Midyanti, S. Bahri, and H. Midyanti, “NON-CASH FOOD ASSISTANCE PROGRAM BENEFICIARIES BASED ON COPRAS AND CODAS”, jitk, vol. 8, no. 2, pp. 111 - 116, Feb. 2023.
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