Implementation of the K-Means Clustering for Teacher Performance Assessment Grouping (PKG) at MI Bani Hasyim Cerme

Authors

  • Bagus Firmansyah University of Muhammadiyah Gresik
  • Umi Chotijah University of Muhammadiyah Gresik
(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i1.180

Keywords:

teacher, teacher performance assessment, K-Means clustering, MI Bani Hasyim, web system

Abstract

Evaluation of teacher performance at MI Bani Hasyim Cerme still uses the manual method. Using office applications such as excel and word results in a significant accumulation of data that makes it difficult for school principals to calculate scores and evaluate the results of clustering or teacher performance scores, so it is wasteful of energy, time, and cost. The k-Means clustering method is expected to facilitate the clustering process of teacher performance values ​​as a source of information and make it easy for school principals to classify teacher performance results. This research aims to obtain clustering values ​​on teacher performance assessment data and to replace the teacher performance assessment system at MI Bani Hasyim, which was previously carried out conventionally into a web-based system. The results of this study are the clustering values ​​of teacher performance assessment and a web-based teacher performance appraisal system. It is expected to facilitate the process of evaluating teacher performance in the Bani Hasyim primary school in the future.

Downloads

Download data is not yet available.

Author Biography

Bagus Firmansyah, University of Muhammadiyah Gresik

Informatics Engineering

References

Hung, M. C., Wu, J., Chang, J. H., & Yang, D. L. (2005). An efficient k-means clustering algorithm using simple partitioning. Journal of Information Science and Engineering, 21(6), 1157–1177.

Imantika, D., Bachtiar, F. A., & Rokhmawati, R. I. (2019). Penerapan metode k-means clustering dan analytical hierarchy process (ahp) untuk pengelompokan kinerja guru dan karyawan pada sma brawijaya smart school. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer J-PTIIK, 3(8), 7382–7390. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5958

Lopis, M. Y. (2016). Rancang Bangun Sistem Penilaian Kinerja Guru Studi Kasus : SMK N 1 Bancak Peneliti : Program Studi Pendidikan Teknik Informatika dan Komputer Fakultas Teknologi Informasi Universitas Kristen Satya Wacana. 702011057.

Madhulatha, S. (2012). An overview of clustering methods. In IOSR Journal of Engineering (Vol. 2, Issue 4, pp. 719–725). https://doi.org/https://doi.org/10.48550/arXiv.1205.1117

Muhiddinur, K. (2019). Guru : Suatu Kajian Teoritis dan Praktis (viii). AURA. http://repo.iainbukittinggi.ac.id/id/eprint/131

Ndehedehe, C., Simeon, O., & Ekpa, A. (2013). Spatial Image Data Mining Using K-Means Analysis: A Case Study of Uyo Capital City, Nigeria. International Journal of Advanced Research, 1(8), 1–6.

Nurzahputra, A., Muslim, M. A., & Khusniati, M. (2017). Penerapan Algoritma K-Means Untuk Clustering Penilaian Dosen Berdasarkan Indeks Kepuasan Mahasiswa. Techno.Com, 16(1), 17–24. https://doi.org/10.33633/tc.v16i1.1284

Ong, J. O. (2013). Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university. Jurnal Ilmiah Teknik Industri, vol.12, no(juni), 10–20. https://doi.org/https://doi.org/10.23917/jiti.v12i1.651

Panjaitan, M., & Sitompul, D. (2015). Implementasi Algoritma K-Means Dan Analytic Hierarchy Process ( AHP ) Untuk Klasterisasi Guru Dan Memilih Guru Terbaik ( Studi Kasus : SMA Santo Yoseph Medan ). Fasilkom-Ti Usu, 1–13.

Pribadi, W. W., Yunus, A., & Sartika Wiguna, A. (2022). Perbandingan Metode K-Means Euclidean Distance Dan Manhattan Distance Pada Penentuan Zonasi Covid-19 Di Kabupaten Malang. Jurnal Mahasiswa Teknik Informatika), 6(2), 493–500.

Saranya, & Punithavalli. (2011). An Efficient Centroid Selection Algorithm for K-Means Clustering. International Journal of Management, IT and Engineering, 130–140.

Sartika, D., & Jumadi, J. (2019). Seminar Nasional Teknologi Komputer & Sains (SAINTEKS) Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: Universitas Dehasen Bengkulu). 703–709. https://seminar-id.com/semnas-sainteks2019.html

Schuh, G., Reinhart, G., Prote, J. P., Sauermann, F., Horsthofer, J., Oppolzer, F., & Knoll, D. (2019). Data mining definitions and applications for the management of production complexity. Procedia CIRP, 81, 874–879. https://doi.org/10.1016/j.procir.2019.03.217

Sukrianto, D. (2016). Penerapan Data Mining Untuk Kinerja Dosen Menggunakan Metode K–Means Clustering (Studi Kasus Di Amik Mahaputra Riau). Jurnal PI-Cache, 5, No 1(Dm), 54–63.

Yaniar, N. S. (2011). Perbandingan Ukuran Jarak pada Proses Pengenalan Wajah Berbasis Principal Component Analysis ( PCA ). Proceeding Seminar Tugas Akhir Jurusan Teknik Elektro FTI‐ITS, 1–6.

Downloads

Published

2022-12-15

How to Cite

Firmansyah, B., & Chotijah, U. (2022). Implementation of the K-Means Clustering for Teacher Performance Assessment Grouping (PKG) at MI Bani Hasyim Cerme. Jurnal Riset Informatika, 5(1), 55–62. https://doi.org/10.34288/jri.v5i1.180