Implementasi k-Means Clustering untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan

Diwa Oktario Dacwanda; Yessica Nataliani

doi:10.24246/aiti.v18i2.125-138

Authors

Diwa Oktario Dacwanda Universitas Kristen Satya Wacana
Yessica Nataliani Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.24246/aiti.v18i2.125-138

Keywords:

student achievements, clustering, k-means, knowledge, skill

Abstract

Student success is a reflection of the quality of education. The quality and student performance need to be improved through educational facilities, infrastructure, and human resources. Information system is a technology that can be useful in improving the students’ quality and achievement, by analyzing the student achievements. This analysis is carried out by classifying student achievements from their ability to understand the courses. The data includes 266 students with 12 courses, where each course has two scores, i.e., knowledge score and skill score, based on the 2013 curriculum. This study uses k-means clustering method, where the number of clusters is determined by the Davies Bouldin validity index. Three groups of student achievements are generated based on academic scores, namely high, moderate, and low. As a comparison, the grouping of student achievements is also done by computing the knowledge scores only. About 10.15 percent of all students move to other cluster when calculated with knowledge scores only. As the result, the grouping of students with the knowledge and skill scores can improve the student achievements.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

S. Budi, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis, Ed. 1. Yogyakarta: Graha Ilmu, 2007.

S. Agustina, D. Yhudo, H. Santoso, N. Marnasusanto, A. Tirtana, and F. Khusnu, “Clustering Kualitas Beras Berdasarkan Ciri Fisik Menggunakan Metode K-Means Algoritma,” Clust. K-Means, pp. 1–7, 2012.

G. Abdurrahman, “Clustering Data Ujian Tengah Semester (UTS) Data Mining Menggunakan Algoritma K-Means,” J. Sist. dan Teknol. Inf. Indones., vol. 1, no. 1, pp. 71–79, 2016.

A. I. Warnilah, “Analisis Algoritma K-Means Clustering untuk Pemetaan Prestasi Siswa,” Indones. J. Comput. Inf. Technol., vol. 1, no. 1, pp. 83–95, 2016.

A. S. Kusuma and K. S. Aryati, “Sistem Informasi Akademik Serta Penentuan Kelas Unggulan Dengan Metode Clusttering Dengan Algoritama K-Means di SMP Negeri 3 Ubud,” J. Sist. Inf. dan Komput. Terap. Indones., vol. 1, no. 3, pp. 143–152, 2019.

C. Purnamaningsih, R. Saptono, and A. Aziz, “Pemanfaatan Metode K-Means Clustering dalam Penentuan Penjurusan Siswa SMA,” J. Teknol. Inf. ITSmart, vol. 3, no. 1, pp. 27–33, 2016.

M. A. W. K. Murti, “Penerapan Metode K-Means Clustering Untuk Mengelompokkan Potensi Produksi Buah–Buahan Di Provinsi Daerah Istimewa Yogyakarta,” Universitas Sanata Dharma, 2017.

E Mulyasa, Pengembangan dan Implementasi Kurikulum 2013. Bandung: Remaja Rosadakarya, 2017.

Ediyanto, N. Mara, and N. S. Intisari, “Pengklasifikasian Karakteristik dengan Metode K-Means Cluster Analysis,” Bul. Ilm. Mat. Stat. dan Ter., vol. 02, no. 2, pp. 133–136, 2013.

C. K. R. Charu C. Aggarwal, DATA Algorithms and Applications. Florida: Chapman and Hall/CRC, 2014.

Y. S. Thakare and S. B. Bagal, “Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics,” Int. J. Comput. Appl., vol. 110, pp. 12–16, 2015.

J. P. Jiawei Han, M. Kamber, Data Mining: Data Mining Concepts and Techniques. Massachusetts: Morgan Kaufmann, 2011.