Testing the K-Means Clustering Algorithm in Processing Student Assignment Grades Using the RapidMiner Application

Pandi Barita Nauli Simangunsong; Mamed Rofendi Manalu

Authors

Pandi Barita Nauli Simangunsong Politeknik LP3I Medan
Mamed Rofendi Manalu Universitas Imelda Medan

Keywords:

educational data analysis, K-Means Clustering, RapidMiner, student achievement, learning strategies

Abstract

In the current era of computers and the internet, educational data analysis has become very important to optimize the teaching and learning process. This study focuses on the use of the K-Means clustering algorithm used by the RapidMiner application to group assignment grades given to students during one academic semester. The goal is to discover patterns of achievement and areas that require intervention. The results show that the algorithm is very effective in identifying groups of students based on their performance and dividing them into middle, high, and those who need help. In short, the use of K-Means Clustering with RapidMiner offers a useful analytical approach for education. This allows for a more customized learning approach that is based on analysis of student achievement.

References

A. Rizki and I. Purnomo, "Optimalisasi Pengajaran Melalui Analisis Data Pendidikan: Tinjauan Konsep dan Praktik," Jurnal Pendidikan dan Teknologi Informasi, vol. 9, no. 2, pp. 110-119, 2022.

L. Darmawan and F. Hartono, "Pendekatan Data Driven dalam Pendidikan: Sebuah Tinjauan Umum," Jurnal Teknologi Informasi dan Pendidikan, vol. 7, no. 1, pp. 45-53, 2021.

Y. Rizal and Z. Anwar, "Metode K-Means Clustering: Sebuah Ulasan Teknik dan Aplikasinya," Konferensi Nasional Teknologi Informasi dan Komunikasi, pp. 210-215, 2019.

S. Pratama and H. Irawan, "Aplikasi RapidMiner dalam Analisis Data Industri: Studi Kasus," Jurnal Ilmu Komputer dan Industri, vol. 4, no. 2, pp. 78-85, 2020.

Putra, B. J. M., & Yuniarti, D. A. F. (2020). Analisis Hasil Belajar Mahasiswa Dengan Clustering Menggunakan Metode K-Means. Poros Teknik, 12(2), 49-58.

Dacwanda, D. O., & Nataliani, Y. (2021). Implementasi k-Means Clustering Untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan. AITI, 18(2), 125-138.

Sembiring, S. N. B., Winata, H., & Kusnasari, S. (2022). Pengelompokan Prestasi Siswa Menggunakan Algoritma K-Means. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 1(1), 31-40.

Purwayoga, V. (2021). Optimasi Jumlah Cluster pada Algoritme K-Means untuk Evaluasi Kinerja Dosen. Jurnal Informatika Universitas Pamulang, 6(1), 118.

Winarta, A., & Kurniawan, W. J. (2021). Optimasi cluster k-means menggunakan metode elbow pada data pengguna narkoba dengan pemrograman python. JTIK (Jurnal Teknik Informatika Kaputama), 5(1), 113-119.