Implementasi Algoritma K-Means Clustering untuk Mengelompokkan Siswa Berdasarkan Nilai sebagai Evaluasi Pembelajaran
Keywords:
Academic Performance, Cluster, K-Means, Student Learning EvaluationAbstract
Academic achievement is a measure of students' learning outcomes, encompassing aspects of knowledge and skills. Academic performance serves as a crucial indicator in evaluating students' learning progress. MAS Al-Wasliyah Petatal is committed to providing quality education but still faces limitations in applying technology to evaluate student learning. The current evaluation process relies on teachers' subjective assessments, which restricts the information about students' progress. Therefore, the implementation of machine learning is proposed as a solution to enhance objectivity in student learning evaluation through more effective data processing. The method used is the K-Means Clustering algorithm, which can group or classify data based on specific patterns. This study aims to evaluate the extent to which machine learning can process student learning evaluation data through the analysis results obtained from the clustering process, which are then used as benchmarks to improve the evaluation system and provide feedback for students needing improvement in their academic performance. The data used comprises students' grades from the odd semester of the 2024/2025 academic year, with a total of 210 data points. The clustering results produced three clusters: the "good" cluster with 60 students, the "average" cluster with 99 students, and the "low" cluster with 51 students.
References
F. K. Ngguna and A. A. Pekuwali, “Penggunaan Algoritma K-Means Untuk Menganalisis Performa Siswa Dalam Pembelajaran Matematika di SMP Negeri Satap Lambakara,” pp. 553–567, 2023.
V. Elmanda, A. E. Purba, Y. P. A. Sanjaya, D. Julianingsih, and others, “Efektivitas program magang siswa smk di kota serang dengan menggunakan metode cipp di era adaptasi new normal pandemi covid-19,” ADI Bisnis Digit. Interdisiplin J., vol. 3, no. 1, pp. 5–15, 2022.
M. Mirnawati and F. Firman, “Penerapan Teknik Clustering Dalam Mengembangkan Kemampuan Menulis Karangan Deskripsi Siswa Kelas IV MI Pesanten Datuk Sulaiman Palopo,” J. Stud. Guru dan Pembelajaran, vol. 2, no. 2, pp. 165–177, 2019, doi: 10.30605/jsgp.2.2.2019.1373.
A. D. Sidik and A. Ansawarman, “Prediksi Jumlah Kendaraan Bermotor Menggunakan Machine Learning,” Formosa J. Multidiscip. Res., vol. 1, no. 3, pp. 559–568, 2022, doi: 10.55927/fjmr.v1i3.745.
K. Setiawan and Y. Y. A. Saputry, “Clustering Data Calon Siswa Baru Menggunakan Metode K-Means di Pusat Pengembangan Anak Fajar Baru Cengkareng,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 8, no. 1, pp. 75–83, 2024, doi: 10.35870/jtik.v8i1.1426.
R. V. Rahadyan and R. A. Widyanto, “Literature Review: Implementation of Machine Learning in E Learning Systems,” Urecol, pp. 723–729, 2022.
S. Butsianto and N. T. Mayangwulan, “Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering,” J. Nas. Komputasi dan Teknol. Inf., vol. 3, no. 3, pp. 187–201, 2020, doi: 10.32672/jnkti.v3i3.2428.
D. A. Kusuma and A. D. P. Wicaksono, “Analisis Klastering Dampak Lingkungan Berdasarkan Konsumsi Energi Perusahaan Berbasis Industri 4.0 Menggunakan Metode Crisp-Dm,” POSITIF J. Sist. dan Teknol. Inf., vol. 9, no. 2, pp. 130–135, 2023, doi: 10.31961/positif.v9i2.2050.
S. Namboori, “Forecasting Carbon Dioxide Emissions in the United States using Machine Learning MSc Research Project Data Analytics,” pp. 1–25, 2019.
F. J. Hamu, D. Wea, and N. Setiyaningtiyas, “Faktor-Faktor yang Memperngaruhi Kinerja Akademik Mahasiswa : Analisis Structural Equation Model,” J. Paedagogy, vol. 10, no. 1, p. 175, 2023, doi: 10.33394/jp.v10i1.6473.
B. Siswa, J. Hutahaean, and S. Amelia, “Teknologi Pendidikan Penggunaan Teknologi Big Data Untuk Menganalisis Perilaku Teknologi Pendidikan,” vol. 3, no. 1, pp. 152–160, 2024, doi: 10.56854/tp.v3i1.232.
R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 109–116, 2021, doi: 10.30871/jaic.v5i1.3237.
Y. Suhanda, I. Kurniati, and S. Norma, “Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik,” J. Teknol. Inform. dan Komput., vol. 6, no. 2, pp. 12–20, 2020, doi: 10.37012/jtik.v6i2.299.
N. A. Az-Zahra and D. B. Arianto, “Analisis Perbandingan Algoritma Regresi Linear dan Decision Tree pada Prediksi Harga Rumah,” pp. 1–5.
I. Santoso, Windu Gata, and Atik Budi Paryanti, “Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Sentimen Analisis Komisi Pemilihan Umum,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 364–370, 2019, doi: 10.29207/resti.v3i3.1084.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jihan Aulia Putri Fahdrina, Eva Lestari, Dila Sari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
