SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA GURU MENGGUNAKAN IMPROVED K-MEANS CLUSTERING DAN METODE SAW

Authors

  • Mawarni Mawarni Universitas Samudra
  • Fitri Rezky Hamzani Universitas Samudra
  • Ulya Nabilla Universitas Samudra
  • Intan Sari Universitas Samudra
  • Masthura Masthura Universitas Samudra

DOI:

https://doi.org/10.54314/jssr.v9i3.6707

Keywords:

k-means clustering, simple additive weighting (SAW), teacher performance assessment, VBA Excel

Abstract

Abstract: School A Banjarmasin is a boarding school that motivates teachers through exemplary teacher selection and salary increases based on performance clusters. However, in 2022–2023, teacher performance appraisal was not conducted due to the absence of a supporting system and historical performance data, resulting in equal salary increases. To address this issue, this study proposes the development of a Decision Support System (DSS) that integrates Improved K-Means Clustering for grouping salary increases and the Simple Additive Weighting (SAW) method for determining exemplary teachers. The system is implemented using Visual Basic for Applications (VBA) in Microsoft Excel. The data used in this study consist of teacher performance assessment results for the 2023/2024 academic year, evaluated across four assessment dimensions comprising eleven criteria. The DSS produces two outputs. Based on school-defined standards, two outputs were generated. The first output indicates suboptimal performance, with three populated clusters, while the fourth cluster contains no members due to data distribution. The second output represents the author's recommended clustering, with a silhouette score evaluation value of 0.5125, indicating optimal visibility between clusters, ensuring four salary increase clusters. Overall, the proposed DSS provides more accurate, objective, and systematic calculations and rankings, thereby supporting decision makers in evaluating teacher performance and determining salary increases more effectively.

Keywords: k-means clustering, simple additive weighting (SAW), teacher performance assessment, VBA Excel

 

Abstrak: Sekolah A Banjarmasin merupakan sekolah berasrama yang memotivasi guru melalui pemilihan guru teladan dan kenaikan gaji berdasarkan klaster kinerja. Namun, pada tahun 2022–2023, penilaian kinerja guru tidak dilaksanakan karena ketiadaan sistem pendukung dan data historis kinerja, sehingga kenaikan gaji diberikan secara merata. Untuk mengatasi masalah ini, penelitian ini mengusulkan pengembangan Sistem Pendukung Keputusan (SPK) yang mengintegrasikan metode Improved K-Means Clustering untuk pengelompokan kenaikan gaji dan metode Simple Additive Weighting (SAW) untuk menentukan guru teladan. Sistem ini diimplementasikan menggunakan Visual Basic for Applications (VBA) pada Microsoft Excel. Data yang digunakan dalam penelitian ini mencakup hasil penilaian kinerja guru tahun ajaran 2023/2024, yang dievaluasi berdasarkan empat dimensi penilaian yang terdiri dari sebelas kriteria. Sistem ini menghasilkan dua keluaran. Keluaran pertama menunjukkan kinerja yang kurang optimal, dengan tiga klaster yang terisi, sementara klaster keempat tidak memiliki anggota akibat distribusi data. Keluaran kedua merepresentasikan hasil pengelompokan yang disarankan oleh penulis, dengan nilai evaluasi silhouette score sebesar 0,5125 yang menunjukkan pemisahan optimal antar-klaster serta memastikan terbentuknya empat klaster kenaikan gaji. Secara keseluruhan, SPK yang diusulkan memberikan perhitungan dan pemeringkatan yang lebih akurat, objektif, dan sistematis, sehingga membantu pengambil keputusan dalam mengevaluasi kinerja guru dan menentukan kenaikan gaji secara lebih efektif.

Kata kunci: k-means clustering, simple additive weighting (SAW), penilaian kinerja guru, VBA Excel

Downloads

Download data is not yet available.

References

S. Mustoip et al., Psikologi Pendidikan.

HDF Publishing, 2023.

S. Andriani, N. Kesumawati, and M. Kristiawan, “The influence of transformational leadership and work motivation on teachers’ performance,” Int. J. Sci. Technol. Res., vol. 7, no. 7, pp. 2277–8616, 2018.

S. M. T. Comighud and M. J. Arevalo, “Motivation in relation to teachers’ performance,” in Proc. UBT Int. Conf., 2021, p. 507.

E. S. Taylor and J. H. Tyler, “The effect of evaluation on teacher performance,” Amer. Econ. Rev., vol. 102, no. 7, pp. 3628–3651, 2012.

M. Ahmed, R. Seraj, and S. M. S. Islam,“The K-means algorithm: A comprehensive survey and performance evaluation,” Electronics, vol. 9, no. 8, p. 1295, 2020.

E. Turban, R. Sharda, and D. Delen, Decision Support and Business Analytics Systems, 11th ed. New York, NY, USA: Pearson, 2021.

M. S. Scott Morton and F. G. W. Keen, Decision Support Systems: An Organizational Perspective, updated ed. Boston, MA, USA: Harvard Business Review Press, 2020.

S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk Pendukung Keputusan, ed. revisi. Yogyakarta, Indonesia: Graha Ilmu, 2020.

A. Alinezhad and A. Amini, Sensitivity Analysis of SAW Method in MADM Problems, Cham, Switzerland: Springer, 2021.

P. Liu, M. Abdullah, and F. Jin, “Normalization Techniques in Simple Additive Weighting for Decision Making,” Applied Soft Computing, vol. 103, pp. 107–118,

T. L. Saaty and L. Vargas, “Decision Making with Multi-Criteria Methods: Applications and Theory,” International Journal of Decision Support Systems, vol. 5, no. 1, pp. 1–15, 2020.

A. Jain, “Unsupervised Learning and Clustering: Recent Advances,” Pattern Recognition Letters, vol. 138, pp. 4–13, 2020.

S. Na, L. Xumin, and G. Yong, “Research on K-Means Clustering Algorithm: An Improved K-Means Clustering Algorithm,” Journal of Software, vol. 15, no. 2, pp. 112–121, 2021.

Y. Wang, Z. Li, and H. Chen, “An Improved K-Means Clustering Algorithm Based on Initial Center Optimization,” IEEE Access, vol. 9, pp. 123456–123465, 2021.

M. Walkenbach, Excel VBA Programming for Dummies, 8th ed. Hoboken, NJ, USA: Wiley, 2022.

R. Praningki, A. Nugroho, and D. Kurniawan, “Decision support system for social welfare data selection using K-Means clustering and simple additive weighting,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 2, pp. 85–94, 2023.

Terttiaavini, “Hybrid K-Means clustering and simple additive weighting method for MSME priority determination,” J. Appl. Decis. Support Syst., vol. 5, no. 1, pp. 15–24, 2024.

A. Laksono, B. Santoso, and R. Wibowo, “Inpatient data clustering using K-Means algorithm for healthcare decision support,” J. Health Inf. Syst., vol. 6, no. 1, pp. 1–10, 2024.

S. Saha, “Prevention of Empty Clusters and Incomplete Data Problems using Modified K-Means and Gaussian Mixture Model”, International Journal of Computer Sciences and Engineering, vol. 11, no. 1, pp. 184–189, 2023.

Downloads

Published

2026-06-30

Issue

Vol. 9 No. 3 (2026): June 2026

Section

Artikel

License

Copyright (c) 2026 Mawarni Mawarni, Fitri Rezky Hamzani, Ulya Nabilla, Intan Sari, Masthura Masthura

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite

SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA GURU MENGGUNAKAN IMPROVED K-MEANS CLUSTERING DAN METODE SAW. (2026). JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 9(3), 4893-4901. https://doi.org/10.54314/jssr.v9i3.6707

Most read articles by the same author(s)