MCDM-AHP and CODAS Collaboration Techniques for Selection of Expert Education Personnel
DOI:
10.33395/sinkron.v9i2.14182Keywords:
CODAS, Collaboration, Expert education personnel , MCDM-AHP, Selection.Abstract
Educational progress is largely determined by human resources who have the best qualifications, with the ability of human resources to provide hope for educational development for a creative and potential future. The ability of human resources to create various variants of knowledge that can be developed to enlighten the progress of thinking through education to create expert education personnel. The aim of this research is to provide techniques to guarantee the quality of the selection process for expert education personnel for the competitive progress of mastering educational technology who are able to independently increase the creative and potential thinking of graduates. To achieve this, of course, strict collaboration techniques are needed in the selection process to obtain expert education personnel. The method proposed in this research is MCDM-AHP in collaboration with CODAS. These two methods can collaborate in providing guarantees for an optimal selection process for education personnel through eight selected assessment criteria and twelve alternatives. From the results obtained, the highest priority was obtained by ALT10 with a weight of 0.229. This gain goes through the stages of normalizing criteria and alternatives with the optimization results of both. With the research results that have been described in detail, the collaboration of the MCDM-AHP and CODAS methods can be used as a measuring tool for optimal assessment of the acquisition of decision support results and can be used as a comparison with other methods for measuring the level of optimization of results.
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Akmal Siahaan, M. (2022). Sistem Pendukung Keputusan Penilaian Kinerja Pegawai Negeri Sipil Menggunakan Metode (Combinative Distance – Based Assessment) Pada Kantor Camat Sei Kepayang. Bulletin of Information Technology (BIT), 3(3), 195–205. https://doi.org/10.47065/bit.v3i3.336
Akmaludin, A., Sihombing, E. G., Rinawati, R., Handayanna, F., Sari Dewi, L., & Arisawati, E. (2023). Generation 4.0 of the programmer selection decision support system: MCDM-AHP and ELECTRE-elimination recommendations. International Journal of Advances in Applied Sciences, 12(1), 48. https://doi.org/10.11591/ijaas.v12.i1.pp48-59
Akmaludin, Samudi, Palasara, N., Harmono, F. P., Widianto, K., & Muharrom, M. (2023). MCDM-AHP and PROMETHEE methods integrated for base service strategy vendor evaluation and selection. International Journal of Advances in Applied Sciences, 12(4), 384–395. https://doi.org/10.11591/ijaas.v12.i4.pp384-395
Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), 445–459. https://doi.org/10.1142/S0218488506004114
Araújo, J., Bentz, W., Cameron, P. J., Kinyon, M., & Konieczny, J. (2022). Matrix theory for independence algebras. Linear Algebra and Its Applications, 642, 221–250. https://doi.org/10.1016/j.laa.2022.02.021
Aro, J. L., Selerio, E., Evangelista, S. S., Maturan, F., Atibing, N. M., & Ocampo, L. (2022). Fermatean fuzzy CRITIC-CODAS-SORT for characterizing the challenges of circular public sector supply chains. Operations Research Perspectives, 9(July), 100246. https://doi.org/10.1016/j.orp.2022.100246
Badi, I., & Kridish, M. (2020). Landfill site selection using a novel FUCOM-CODAS model: A case study in Libya. Scientific African, 9. https://doi.org/10.1016/j.sciaf.2020.e00537
Bao, S., Tang, L., Li, K., Ning, P., Peng, J., & Guo, H. (2015). To appear in : Received Date : Accepted Date : Abstract : Journal of Colloid and Interface Science, Ii, 110495. https://doi.org/10.1016/j.jfa.2024.110495
Baraibar-Diez, E., Odriozola, M. D., & Llorente, I. (2024). Exploring learning congruence and the availability of diverse educational resources: A study conducted in the field of management education. International Journal of Management Education, 22(2), 100985. https://doi.org/10.1016/j.ijme.2024.100985
Bezushchak, O. (2022). Automorphisms and derivations of algebras of infinite matrices. Linear Algebra and Its Applications, 650, 42–59. https://doi.org/10.1016/j.laa.2022.05.020
Bhol, S. G., Mohanty, S., & Pattnaik, P. K. (2024). Machine Learning as a Service Cloud Selection: An MCDM Approach for Optimal Decision Making. Procedia Computer Science, 233(2023), 909–918. https://doi.org/10.1016/j.procs.2024.03.280
Brodny, J., & Tutak, M. (2023). Assessing the energy security of European Union countries from two perspectives – A new integrated approach based on MCDM methods. Applied Energy, 347(April). https://doi.org/10.1016/j.apenergy.2023.121443
Bunimovich, L., & Shu, L. (2018). Generalized eigenvectors of isospectral transformations, spectral equivalence and reconstruction of original networks. Linear Algebra and Its Applications, 551, 104–124. https://doi.org/10.1016/j.laa.2018.04.007
Caliph, S. M., & Lee, C. Y. (2024). Online teaching and learning of a pharmacy curriculum designed for active learning and professional skills development – A report of students’ perceptions and learning experience in two international campuses. Currents in Pharmacy Teaching and Learning, 16(2), 119–123. https://doi.org/10.1016/j.cptl.2023.12.017
Calvin, R. (2024). ScienceDirect Hybrid MCDM Career Recommendation System for Information System Student Using AHP , VIKOR and Weighted Euclidean Distance. Procedia Computer Science, 234, 364–372. https://doi.org/10.1016/j.procs.2024.03.016
Cooper, O. (2017). the Magic of the Analytic Hierarchy Process (Ahp). International Journal of the Analytic Hierarchy Process, 9(3), 372–379. https://doi.org/10.13033/ijahp.v9i3.519
Geletu, G. M., & Mihiretie, D. M. (2023). Professional accountability and responsibility of learning communities of practice in professional development versus curriculum practice in classrooms: Possibilities and pathways. International Journal of Educational Research Open, 4(December 2022), 100223. https://doi.org/10.1016/j.ijedro.2022.100223
Hamidah, M., Mohd Hasmadi, I., Chua, L. S. L., Yong, W. S. Y., Lau, K. H., Faridah-Hanum, I., & Pakhriazad, H. Z. (2022). Development of a protocol for Malaysian Important Plant Areas criterion weights using Multi-criteria Decision Making - Analytical Hierarchy Process (MCDM-AHP). Global Ecology and Conservation, 34(October 2021), e02033. https://doi.org/10.1016/j.gecco.2022.e02033
Nalbant, K. G. (2024). A methodology for personnel selection in business development: An interval type 2-based fuzzy DEMATEL-ANP approach. Heliyon, 10(1), e23698. https://doi.org/10.1016/j.heliyon.2023.e23698
Peng, X., & Garg, H. (2018). Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Computers and Industrial Engineering, 119(288), 439–452. https://doi.org/10.1016/j.cie.2018.04.001
Prasetyo et al., 2021. (2021). Sistem Seleksi Penerima Bantuan Program Keluarga Harapan. 09(03).
Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8
Saputra, N. A. B., & Purba, H. S. (2022). Rancangan Sistem Manajemen Skripsi Berbasis Web Menggunakan Metode Rapid Application Development (RAD). JURIKOM (Jurnal Riset Komputer), 9(5), 1621. https://doi.org/10.30865/jurikom.v9i5.5012
Soukalová, R., & Gottlichová, M. (2015). The Impact of Effective Process of Higher Education on the Quality of Human Resources in the Czech Republic. Procedia - Social and Behavioral Sciences, 174, 3715–3723. https://doi.org/10.1016/j.sbspro.2015.01.1104
Spain, R. D., Hedge, J. W., Ohse, D., & White, A. (2022). The need for research-based tools for personnel selection and assessment in the forensic sciences. Forensic Science International: Synergy, 4(September 2020), 100213. https://doi.org/10.1016/j.fsisyn.2021.100213
Tian, W., Sun, S., Wu, B., Yu, C., Cui, F., Cheng, H., You, J., & Li, M. (2023). Development and validation of a deep learning algorithm for pattern-based classification system of cervical cancer from pathological sections. Heliyon, 9(8), e19229. https://doi.org/10.1016/j.heliyon.2023.e19229
Ulandari, N. W. A., Pivin Suwirmayanti, N. L. G., Warma Putra, I. P., & Astiti, N. M. (2021). Spk Seleksi Penerima Beasiswa pada ITB Stikom Bali dengan Metode Codas. Jurnal Teknik Informatika UNIKA Santo Thomas, 06, 206–216. https://doi.org/10.54367/jtiust.v6i2.1497
Vermunt, J. D., Vrikki, M., Dudley, P., & Warwick, P. (2023). Relations between teacher learning patterns, personal and contextual factors, and learning outcomes in the context of Lesson Study. Teaching and Teacher Education, 133(April), 104295. https://doi.org/10.1016/j.tate.2023.104295
Wątróbski, J., Bączkiewicz, A., Król, R., & Sałabun, W. (2022a). Green electricity generation assessment using the CODAS-COMET method. Ecological Indicators, 143(September). https://doi.org/10.1016/j.ecolind.2022.109391
Wątróbski, J., Bączkiewicz, A., Król, R., & Sałabun, W. (2022b). Green electricity generation assessment using the CODAS-COMET method. Ecological Indicators, 143(March). https://doi.org/10.1016/j.ecolind.2022.109391
Ye, Q. (2008). Optimal expansion of subspaces for eigenvector approximations. Linear Algebra and Its Applications, 428(4), 911–918. https://doi.org/10.1016/j.laa.2007.08.021
Yunus, R. M., Samadi, Z., Yusop, N. M., & Omar, D. (2013). Expert Choice for Ranking Heritage Streets. Procedia - Social and Behavioral Sciences, 101, 465–475. https://doi.org/10.1016/j.sbspro.2013.07.220
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