Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa

Nirwana Hendrastuty

doi:10.58602/jima-ilkom.v3i1.26

Nirwana Hendrastuty Universitas Teknokrat Indonesia

DOI: https://doi.org/10.58602/jima-ilkom.v3i1.26

Keywords: Clustering, Data Mining, Evaluation, K-Means, Student Learning

Abstract

Evaluation of student learning outcomes is a critical process in education that aims to measure the achievement of learning objectives. Through various methods such as tests, projects, and observations, teachers can assess students' understanding, skills, and progress in the subject matter. The purpose of applying data mining using the K-Means Clustering algorithm in evaluating student learning outcomes is to identify patterns that may be hidden in learning outcome data, divide students into groups based on their level of achievement or learning characteristics, and provide valuable insights to teachers and education stakeholders. The results of clustering student learning assessment data can uncover patterns that are beneficial to educators and school administrators. Analysis of these clusters can reveal information about achievement trends, trends in success or difficulty in specific subjects, as well as allow identification of students who need additional help. Grouping of cluster results based on student assessment data with k-means obtained 2 groups of students, namely Diligent students with group C0 and group students Very Diligent with group C1. The C0 group of Diligent students consists of 63 students and the C1 group consists of 91 Very Diligent students. The silhouette score test results for cluster 2 are as high as 0.9168 and show that grouping data into these groups is better, the use of silhouette score as an evaluation metric provides useful guidance in determining the optimal number of clusters in clustering analysis and data interpretation.

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References

M. Syahril, K. Erwansyah, and M. Yetri, “Penerapan Data Mining untuk menentukan pola penjualan peralatan sekolah pada brand wigglo dengan menggunakan algoritma apriori,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 3, no. 1, pp. 118–136, 2020.

M. J. Zaki and W. J. Meira, Data Mining and Machine Learning Fundamental Concepts and Algorithms, vol. 53, no. 9. 2020.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.

M. S. Sungkar and M. T. Qurohman, “Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 1166, 2021, doi: 10.30865/mib.v5i3.3116.

A. Zheng, J. Cai, H. Yang, and X. Zhao, “CPGAN: Curve Clustering Architecture Based on Projected Latent Vector of Generative Adversarial Network,” IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers (IEEE), pp. 86765–86776, 2020. doi: 10.1109/access.2020.2992887.

M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration k-means clustering method and elbow method for identification of the best customer profile cluster,” in IOP conference series: materials science and engineering, 2018, vol. 336, no. 1, p. 12017.

M. Cui, “Introduction to the k-means clustering algorithm based on the elbow method,” Accounting, Audit. Financ., vol. 1, no. 1, pp. 5–8, 2020.

A. Yudhistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” J. Artif. Intell. Technol. Inf., vol. 1, no. 1, pp. 20–28, 2023.

N. Noviyanto, “Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 183–188, 2020, doi: 10.31294/p.v22i2.8808.

A. A. Aldino, D. Darwis, A. T. Prastowo, and C. Sujana, “Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency,” in Journal of Physics: Conference Series, 2021, vol. 1751, no. 1, p. 12038.

J. Hutagalung, “Pemetaan Siswa Kelas Unggulan Menggunakan Algoritma K-Means Clustering,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 606–620, 2022.

Q. I. Mawarni and E. S. Budi, “Implementasi Algoritma K-Means Clustering Dalam Penilaian Kedisiplinan Siswa,” J. Sist. Komput. dan Inform., vol. 3, no. 4, pp. 522–528, 2022.

A. Rohmah, F. Sembiring, and A. Erfina, “Implementasi Algoritma K-Means Clustering Analysis Untuk Menentukan Hambatan Pembelajaran Daring (Studi Kasus: Smk Yaspim Gegerbitung),” in Prosiding Seminar Nasional Sistem Informasi dan Manajemen Informatika Universitas Nusa Putra, 2021, vol. 1, no. 01, pp. 290–298.

F. P. Dewi, P. S. Aryni, and Y. Umaidah, “Implementasi Algoritma K-Means Clustering Seleksi Siswa Berprestasi Berdasarkan Keaktifan dalam Proses Pembelajaran,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 7, no. 2, pp. 111–121, 2022.

Z. Nabila, A. R. Isnain, P. Permata, and Z. Abidin, “ANALISIS DATA MINING UNTUK CLUSTERING KASUS COVID-19 DI PROVINSI LAMPUNG DENGAN ALGORITMA K-MEANS,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, pp. 100–108, 2021.