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Determinate Student Final Project Supervisor Based AHP and SAW

Determining competent supervisors for student research projects is one of the factors that play the most important role because it can affect the success of student education, so it deserves attention. However, the process of determining a supervisor is not an easy thing because it involves various complex criteria and sub-criteria for making decisions consistently and objectively. Therefore, we propose AHP and SAW methods be utilized simultaneously with the criteria for education level, educational background, guiding experience, lecturer experience area, publication, guide quota, and student concentration, along with Forty-Three (43) other sub-criteria. This research purpose is to provide knowledge about how the AHP-SAW methods can be utilized together to cover each other's weaknesses in determining supervisors for student research projects. Where the AHP method works to calculate the priority level of criteria and sub-criteria that will be used by the SAW method in forming a matrix of criteria and alternatives and calculates the consistency value of the criteria and sub-criteria, while the SAW method works to calculate the matrix normalization value and ranking value for each alternative by utilizing the value of priority level of the criteria obtained from work of AHP. The results showed that the two methods were able to complement each other in determining the main supervisor of student research projects, with a ranking score of 1,00 for alternative ALec_002 and a co-supervisor ranking score of 0,97 for alternative ALec_007 out of 35 candidates.

Analytical Hierarchy Process, Simple Additive Weighting, Supervisor

Teotino Gomes Soares, Marcelo Fernandes Xavier Cham, Abdullah Bin Zainol Abidin. (2023). Determinate Student Final Project Supervisor Based AHP and SAW. American Journal of Artificial Intelligence, 7(2), 31-39.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. E. R. Arumi, A. Setiawan, and A. Primadewi, “Decision Support System for Determining Thesis Supervisor Using Analytical Hierarchy Arocess (AHP) Method,” J. Phys. Conf. Ser., vol. 1517, no. 1, pp. 1–8, 2020, doi: 10.1088/1742-6596/1517/1/012107.
2. S. J. Armstrong, C. W. Allinson, and J. Hayes, “The Effects of Cognitive Style on Research Supervision: A Study of Student-Supervisor Dyads in Management Education,” Acad. Manag. Learn. Educ., vol. 3, no. 1, pp. 41–63, 2004, doi: 10.5465/amle.2004.12436818.
3. P. C. Burnett, “The supervision of doctoral dissertations using a collaborative cohort model,” Couns. Educ. Superv., vol. 39, no. 1, pp. 46–52, 1999, doi: 10.1002/j.1556-6978.1999.tb01789.x.
4. T. U. of Edinburgh, Code of Practice for Supervisors and Doctoral Students. United Kingdom: The University of Edinburgh, 2022.
5. J. S. Simanungkalit and H. T. Sihotang, “Decision Support System for Selection of Thesis Advisors According to the Field of Science Using the AHP Method,” J. Intell. Decis. Support Syst., vol. 3, no. 4, 2020.
6. M. A. Hasan and D. G. Schwartz, “A multi-criteria decision support system for Ph. D. Supervisor selection: A hybrid approach,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2019-Janua, pp. 1823–1832, 2019, doi: 10.24251/hicss.2019.220.
7. A. J. Latipah, “Application and Evaluation of AHP-ELECTRE Performance in the Determination of the Thesis Supervisor,” J. Sci. Eng., vol. 1, no. 2, pp. 71–74, 2020.
8. S. R. Arifin and J. C. Mintamanis, “Decision Support System for Determining Thesis Supervisor using A Weighted Product (WP) Method,” J. Online Inform., vol. 3, no. 2, pp. 80–85, 2019, doi: 10.15575/join.v3i2.230.
9. M. M. Amin, A. Sutrisman, and Y. Dwitayanti, “Group Decision Support System Model to Determine Supervisor Lecturers for Student Creativity Programs,” Bull. Electr. Eng. Informatics, vol. 12, no. 4, pp. 2484–2494, 2023, doi: 10.11591/eei.v12i4.4784.
10. Sari, F. Ebriyanto, and I. Rusi, “Implementation of Simple Additive Weighting Method in the Determination System of Thesis Supervisor,” J. Ilm. MATRIK, vol. 23, no. 2, pp. 133–141, 2021.
11. S. A. Aklani and Jacky, “Mini Thesis Supervisor Recommender System Using Simple Additive Weighting Algorithms : A Case Study of Universitas Internasional Batam,” J. Inform. dan Sains, vol. 05, no. 02, pp. 153–158, 2022.
12. D. Meidelfi, F. Sukma, D. Chandra, A. Hendri, and S. Jones, “The Implementation of SAW and BORDA Method to Determine the Eligibility of Students ’ Final Project Topic,” Int. J. Informatics Vis., vol. 5, no. 2, pp. 144–149, 2021.
13. M. Khurwolah and Y. Chuttur, “Requirements for an Online Automated Project Allocation System in Higher Education Institutions – A Case Study,” Lett. Inf. Technol. Educ., vol. 3, no. 2, pp. 49–53, 2020.
14. H. R. Dewi, S. Anam, and Marjono, “Allocation of Thesis Supervisor Using Genetic Algorithm,” J. EECCIS, vol. 12, no. 1, pp. 26–32, 2018.
15. T. L. Saaty and L. G. Vargas, Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. New York: Springer, 2012. doi: 10.1007/978-1-4614-3597-6.
16. T. L. Sasty, “How to Make an Decition: The Analytic Hierarchy Process,” Eur. J. Oper. Res., vol. 48, no. 9–26, 1990.
17. T. L. Saaty, “Decision Making with the Analytic Hierarchy Process,” Int. J. Serv. Sci., vol. 1, no. 1, pp. 83–98, 2008.
18. S. H. Kusumadewi, Fuzzy Multi-Attribute Decision Making (Fuzzy MADM). Yogyakatya: Graha Ilmu, 2006.
19. F. Sari, Metode Dalam Pengambilan Keputusan. Yogyakatya: Deepublish Publisher, 2017.
20. I. Kaliszewski and D. Podkopaev, “Simple Additive Weighting — A Metamodel for Multiple Criteria Decision Analysis Methods,” Expert Syst. Appl., 2016, doi: 10.1016/j.eswa.2016.01.042.
21. N. Setiawan et al., “Simple Additive Weighting as Decision Support System for Determining Employees Salary,” Int. J. Eng. Technol., no. August, 2018.
22. D. Nofriansyah and S. Defit, Multi Criteria Decision Making (MCDM) Pada Sistem Penduung Keputusan. Yogyakatya: Deepublish Publisher, 2017.
23. H. Adela, K. A. Jasmi, B. Basiron, M. Huda, and A. Maseleno, “Selection of Dancer Member Using Simple Additive Weighting,” Int. J. Eng. Technol., vol. 7, no. 3, pp. 1096–1107, 2018, doi: 10.14419/ijet.v7i3.11983.
24. I. J. T. Situmeang, S. Hummairoh, M. Harahap, and Mesran, “Application of SAW (Simple Additive Weighting) for the Selection of Campus Ambassadors,” Int. J. Informatics Comput. Sci., vol. 5, no. 1, pp. 21–28, 2021, doi: 10.30865/ijics.v5i1.2847.