Assessment Clusterization Teacher Performance with K-Means Algorithm Clustering and Agglomerative Hierarchical Clustering (AHC)
DOI:
10.33395/sinkron.v9i1.14200Keywords:
Agglomerative Hierarchical Clustering (AHC); Clustering Algorithm; Data Mining; Indonesian Education, Silhouette Score; K-Means; Teacher Performance EvaluationAbstract
Research This aims to do clustering evaluation teacher performance with the application of the K-means clustering algorithm and agglomerative hierarchical clustering (AHC). Background study This is based on needs to increase quality teaching through analysis and evaluation and better teacher performance. The methods applied involving assessment data collection performance from teachers in the environment education local, processed using a second algorithm The results of the research show that the silhouette score value for K-means reached 0.364, while AHC produced a value 0.343. With Thus, K-means is proven more effective in grouping assessment data and teacher performance compared to AHC. The conclusion of the study This confirms the importance of implementation of the K-means algorithm to get more insight into good evaluation teacher performance. Author Ready to do repairs or revisions to the manuscript. This is in accordance with comments and suggestions from the reviewer as a condition beginning. For processing more, carry on.
Downloads
References
Mutaqin, A. M., & Andriyani, W. (2022). Klasterisasi Data Disabilitas Menggunakan Algoritma K-Means. Ijir, 3(1), 25–35.
Dewi, N. L. P. P., Purnama, I. N., & Utami, N. W. (2022). Penerapan Data Mining Untuk Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: STMIK Primakara). Jurnal Ilmiah Teknologi Informasi Asia, 16(2), 105. https://doi.org/10.32815/jitika.v16i2.761
Fitri, E. M., Suryono, R. R., & Wantoro, A. (2023). Klasterisasi Data Penjualan Berdasarkan Wilayah Menggunakan Metode K-Means Pada Pt Xyz. Jurnal Komputasi, 11(2), 157–168. https://doi.org/10.23960/komputasi.v11i2.12582
Gustientiedina, G., Adiya, M. H., & Desnelita, Y. (2019). Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan. Jurnal Nasional Teknologi Dan Sistem Informasi, 5(1), 17–24. https://doi.org/10.25077/teknosi.v5i1.2019.17-24
Hasan, M. (2022). Implementasi supervisi akademik dalam meningkatkan kompetensi dan kinerja guru di ma al ishlah natar dan ma mathlaul anwar cinta mulya. 06, 85–97.
Yulianti, I. D., Hermanto, I. T., & Defriani, M. (2023). RESOLUSI : Rekayasa Teknik Informatika dan Informasi Analisis Clustering Donor Darah dengan Metode Agglomerative Hierarchical Clustering. Media Online), 3(6), 308. https://djournals.com/resolusi
Kamaruddin, I., Sari, M. N., & Andriani, N. (2024). Evaluasi Kinerja Guru : Model dan Metode dalam Meningkatkan Mutu Pendidikan. 06(02), 11349–11358.
MELELO, S. S. (2023). No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title. 5(8), 1–14. https://www.ncbi.nlm.nih.gov/books/NBK558907/
Nurjannah, S., & Nurhadi, A. (2020). Relevansi Tujuan dan Materi Dalam Program Pendidikan dan Pelatihan Pengembangan Guru PAI di Era Digital. Indonesian Jurnal of Islamic Education Management, 3(2), 96–107.
Pamungkas, T. B., Maesaroh, S., & Ardiansyah, P. (2023). Implementasi Data Mining Pada Stok Penggunaan Barang Di Gmf Aeroasia Menggunakan Algoritma K-Means Clustering. Jurnal Ilmiah Sains Dan Teknologi, 7(2), 112–123. https://doi.org/10.47080/saintek.v7i2.2697
Parlambang, B., & Fauziah. (2020). Implementasi Algoritma K-Means Dalam Proses Penilaian Kuesioner Kepada Dosen Guna Mendukung Kepuasan Mahasiswa Terhadap Dosen. Jurnal Ilmiah Teknologi Dan Rekayasa, 25(2), 161–173. https://doi.org/10.35760/tr.2020.v25i2.2719
Selly, J. B., Hauwali, N. U. J., Lantik, V., & ... (2023). Pengembangan Sistem Informasi Berbasis Web Pada Program Studi Pendidikan Fisika Universitas Nusa Cendana. Jurnal Pendidikan …, April, 89–95. https://ojs.cbn.ac.id/index.php/jukanti/article/view/914%0Ahttps://ojs.cbn.ac.id/index.php/jukanti/article/download/914/338
Syafrinal, I., & Febrianti, E. L. (2023). Penerapan Algoritma K-Means Pada Aplikasi Data Mining Untuk Menentukan Pola Penjualan (Studi Kasus: Zahra Mart). Jurnal Digit, 13(1), 31. https://doi.org/10.51920/jd.v13i1.320
Syahara, U., Kurniawati, E., Suhana, M. P., Anggraini, R., & Yandri, F. (2024). Penerapan Metode AHC (Agglomerative Hierarchical Clustering) untuk Klasifikasi Habitat Bentik di Desa pengudang, Kabupaten Bintan. INSOLOGI: Jurnal Sains Dan Teknologi, 3(3), 306–314. https://doi.org/10.55123/insologi.v3i3.3547
Ummah, M. S. (2019). No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title. Sustainability (Switzerland), 11(1), 1–14. http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI
Virgo, I., Defit, S., & Yuhandri, Y. (2020). Klasterisasi Tingkat Kehadiran Dosen Menggunakan Algoritma K-Means Clustering. Jurnal Sistim Informasi Dan Teknologi, 2, 23–28. https://doi.org/10.37034/jsisfotek.v2i1.17
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2025 Rodiatun, Sri Lestari

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