Decision Support System for Teacher Performance Evaluation Using LODECI Weighting and CODAS Method

Fadila Shely Amalia; Muksin Hi Abdullah; Sumanto; Setiawansyah; Junhai Wang

doi:10.34148/teknika.v15i2.1476

Authors

Fadila Shely Amalia Department of Technology Information, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, Indonesia
Muksin Hi Abdullah Department of Environmental Engineering, Faculty of Engineering, Institut Teknologi Gamalama, Ternate, Maluku, Indonesia
Sumanto Department of Informatics, Faculty of Engineering and Informatics, Universitas Bina Sarana Informatika, Jakarta Pusat, Daerah Khusus Ibukota Jakarta
Setiawansyah Department of Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, Indonesia
Junhai Wang Department of Commerce and Circulation, Faculty of Digital Commerce, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, China

DOI:

https://doi.org/10.34148/teknika.v15i2.1476

Keywords:

DSS, LODECI Method, CODAS Method, Teacher Performance Evaluation, MCDM

Abstract

The quality of teacher performance evaluation plays an important role in improving educational outcomes, yet conventional assessment approaches often suffer from subjectivity, inconsistency, and lack of transparent weighting mechanisms. This study proposes a Decision Support System (DSS) model by integrating the LODECI method for objective criteria weighting and the CODAS method for alternative ranking to produce more accurate and data-driven evaluation results. The LODECI method determines criterion weights based on data distribution characteristics, resulting in proportional weights where Classroom Management (0.1881), Pedagogical Competence (0.1825), and Creativity and Innovation (0.1702) are identified as the most influential criteria. Furthermore, the CODAS method evaluates teacher performance using a distance-based approach to the negative ideal solution, producing preference values that enable clear differentiation among alternatives. The ranking results show that A7 – Gina achieves Rank 1 with a value of -0.2399, followed by A3 – Citra in Rank 2 with -0.2181, and A9 – Intan in Rank 3 with -0.1374, indicating their superior performance compared to other alternatives. To ensure robustness, a sensitivity analysis was conducted using 18 threshold (φ) scenarios ranging from 0.1 to 0.95. The results demonstrate that the top-ranked alternatives (A7, A3, and A9) consistently maintain their positions across all scenarios, indicating that the proposed model is stable and not significantly affected by parameter changes. Therefore, the integration of LODECI and CODAS within a DSS framework proves to be effective in producing objective, consistent, and reliable teacher performance evaluations that can support decision-making in educational institutions.

Downloads

Download data is not yet available.

References

[1] “Principal’s Supervision Strategy to Improve Teachers’ Performance Based on Professional Competence,” JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan), vol. 10, no. 1 SE-Articles, pp. 288–309, doi: 10.31851/jmksp.v10i1.18104.

[2] S. Setiawansyah, “Comparative Analysis of CODAS, TOPSIS, and COCOSO Methods Using Objective Weighting in Multi-Criteria Decision Support Systems,” J. Ilm. Inform. dan Ilmu Komput., vol. 5, no. 1 SE-Articles, pp. 25–45, Mar. 2026, doi: 10.58602/jima-ilkom.v5i1.78.

[3] S. Dündar, “Prioritizing the Regional Preferences of Turkish Investors Regarding Foreign Direct Investment by ARLON Method,” Konya J. Eng. Sci., vol. 13, no. 3, pp. 927–946, 2025, doi: 10.36306/konjes.1648279.

[4] G. C. Yalçın, P. Gürol, and K. Kara, “Chapter 6 - Developing a GIS-supported SVN-WENSLO-ARLON hybrid method for bicycle pooling location selection: A case study in Turkey,” M. B. T.-I. U. M. Deveci, Ed. Academic Press, 2025, pp. 103–148. doi: https://doi.org/10.1016/B978-0-443-34160-1.00014-6.

[5] K. Stanković, D. Jelić, N. Tomašević, and A. Krstić, “Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case,” J. Manuf. Syst., vol. 76, pp. 293–313, 2024, doi: https://doi.org/10.1016/j.jmsy.2024.07.015.

[6] N. Hendrastuty, J. Wang, A. Sulistiyawati, D. Darwis, and Y. Jumaryadi, “Combination of MOORA and ITARA Methods in Decision Support Systems for Measuring the Performance of Quality Control Teams,” vol. 6, no. 6, pp. 727–739, 2025, doi: 10.47065/tin.v6i6.8382.

[7] K. Chandrashekar, A. S. Setlur, A. Sabhapathi C, S. S. Raiker, S. Singh, and V. Niranjan, “Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.,” Cancer Inform., vol. 22, p. 11769351221147244, 2023, doi: 10.1177/11769351221147244.

[8] A. Venkateshwaran, I. Kant Deo, and R. K. Jaiman, “MUTE-DSS: A digital-twin-based decision support system for minimizing underwater radiated noise in ship voyage planning,” Ocean Eng., vol. 343, p. 123343, 2026, doi: https://doi.org/10.1016/j.oceaneng.2025.123343.

[9] İ. Otay, S. Çevik Onar, B. Öztayşi, and C. Kahraman, “Evaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodology,” Expert Syst. Appl., vol. 250, p. 123874, 2024, doi: https://doi.org/10.1016/j.eswa.2024.123874.

[10] D. Pamucar, V. Simic, Ö. F. Görçün, and H. Küçükönder, “Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals,” Expert Syst. Appl., vol. 239, p. 122312, 2024, doi: https://doi.org/10.1016/j.eswa.2023.122312.

[11] B. Yagmahan and H. Yılmaz, “An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability,” Environ. Dev. Sustain., vol. 25, no. 1, pp. 96–121, 2023, doi: 10.1007/s10668-021-02044-1.

[12] Gokhan Basar and Oguzhan Der, “Multi-objective optimization of process parameters for laser cutting polyethylene using fuzzy AHP-based MCDM methods,” Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng., vol. 239, no. 4, pp. 2295–2309, Feb. 2025, doi: 10.1177/09544089251319202.

[13] T. Li, J. Sun, and L. Fei, “Application of Multiple-Criteria Decision-Making Technology in Emergency Decision-Making: Uncertainty, Heterogeneity, Dynamicity, and Interaction,” Mathematics, vol. 13, no. 5. 2025. doi: 10.3390/math13050731.

[14] M. Krstić, S. Tadić, P. P. Miglietta, and D. Porrini, “Biodiversity Protection Practices in Supply Chain Management: A Novel Hybrid Grey Best–Worst Method/Axial Distance-Based Aggregated Measurement Multi-Criteria Decision-Making Model,” Applied Sciences, vol. 15, no. 3. 2025. doi: 10.3390/app15031354.

[15] A. Suryadi, A. Adam Muiz, and A. Alpan Hidayat, “Implementation of a Decision Support System for Selecting the Best Supplier Using the SAW Method,” bit-Tech, vol. 7, no. 3 SE-Articles, pp. 928–935, Apr. 2025, doi: 10.32877/bt.v7i3.2255.

[16] A. Fetanat and M. Tayebi, “Fuzzy set–based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective,” Environ. Monit. Assess., vol. 196, no. 12, p. 1267, 2024, doi: 10.1007/s10661-024-13348-w.

[17] D. Abdul, J. Wenqi, A. Tanveer, and I. M. Sharaf, “Analyzing the sustainability factors to prioritize sustainable energy source: A novel integrated MCDM model,” Energy, vol. 337, p. 138637, 2025, doi: https://doi.org/10.1016/j.energy.2025.138637.

[18] G. C. Yalçın, K. Kara, and T. Senapati, “A hybrid spherical fuzzy logarithmic decomposition of criteria importance and alternative ranking technique based on Adaptive Standardized Intervals model with application,” Decis. Anal. J., vol. 11, p. 100441, 2024, doi: https://doi.org/10.1016/j.dajour.2024.100441.

[19] O. Pala, “Assessment of the social progress on European Union by logarithmic decomposition of criteria importance,” Expert Syst. Appl., vol. 238, p. 121846, 2024, doi: https://doi.org/10.1016/j.eswa.2023.121846.

[20] Z. Li, Y. Wang, J. Xie, Y. Cheng, and L. Shi, “Hybrid multi-criteria decision-making evaluation of multiple renewable energy systems considering the hysteresis band principle,” Int. J. Hydrogen Energy, vol. 49, pp. 450–462, 2024, doi: https://doi.org/10.1016/j.ijhydene.2023.09.059.

[21] P. Rani, A. R. Mishra, A. M. Alshamrani, A. F. Alrasheedi, and D. Pamucar, “Adoption of Smart Manufacturing Technologies in Small and Medium Enterprises Using Picture Fuzzy Combinative Distance-Based Assessment Model,” J. Knowl. Econ., 2025, doi: 10.1007/s13132-025-02693-x.

[22] A. Fergina, A. Sukandar, R. N. Salsabila, and A. I. Wulandari, “Decision Support System for Assessing Teacher Performance Using the Simple Additive Weighting (SAW) Method at SMK XYZ,” Engineering Proceedings, vol. 107, no. 1. p. 75, 2025. doi: 10.3390/engproc2025107075.

[23] Y. Vieolita and Cici Ulandari, “Sistem Pendukung Keputusan Penilaian Kinerja Guru Menggunakan Metode TOPSIS,” J. Decis. Support Syst. Res., vol. 2, no. 1 SE-, pp. 29–37, Sep. 2024, doi: 10.64366/dss.v2i1.72.

[24] D. Karyaningsih, “Decision Support System for Teacher Performance Assessment Using the Weighted Product Method ,” Int. J. Inf. Technol. Comput. Sci. Appl., vol. 1, no. 2 SE-New Submission, pp. 110 – 121, Jun. 2023, doi: 10.58776/ijitcsa.v1i2.21.

[25] A. Çilek and O. Şeyranlıoğlu, “Measuring the Financial Performance of Reinsurance Companies in Türkiye with LODECI, CRADIS and AROMAN MCDM Methods TT - LODECI, CRADIS ve AROMAN,” Int. J. Bus. Econ. Stud., vol. 7, no. 1, pp. 1–18, 2025, doi: 10.54821/uiecd.1587675.

[26] D. Tufan and A. Ulutaş, “Supplier Selection in the Food Sector: An Integrated Approach Using LODECI and CORASO Methods,” Spectr. Decis. Mak. Appl., vol. 3, no. 1 SE-Articles, pp. 40–51, Mar. 2025, doi: 10.31181/sdmap31202631.

[27] S. Zeng and C. Yang, “Risk evaluation of livestream e-commerce platforms based on expert trust networks and CODAS,” Expert Syst. Appl., vol. 260, p. 125408, 2025, doi: https://doi.org/10.1016/j.eswa.2024.125408.

[28] S. H. Gurmani, S. Zhang, F. A. Awwad, and E. A. A. Ismail, “Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making,” Eng. Appl. Artif. Intell., vol. 133, p. 108165, 2024, doi: https://doi.org/10.1016/j.engappai.2024.108165.