Stream Clustering for Selection Recommendations Using K-Means Algorithm: A Case Study in the Informatics Study Program

  • Riska Fahmita Anggraini Universitas Bina Darma, Indonesia
  • Siti Sau'da Universitas Bina Darma, Indonesia
Keywords: Software, MK-Stream, K-Means, Clustering, Data Analytic

Abstract

Concentration Stream for a major is a process where students focus their attention on a specific discipline according to their interests. The purpose of specialization is to better orient students to the knowledge they have gained from previous courses, so that they can have a clearer focus. In the Informatics Engineering study program at Bina Darma University there are 3 concentrations, namely: Software Engineering, Network Engineering, Data Analytics. The absence of a system that helps students choose a major concentration makes it quite difficult for students to know their academic abilities. By looking at these problems, this research aims to build a Recommendation system for selecting Mk-Stream Concentrations using the K-Means grouping approach using the K-Means cluster method. Where student academic achievement data from the first semester to the 4th semester is used as a variable in the calculations.

Downloads

Download data is not yet available.

References

A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” J. Tekno Kompak, vol. 15, no. 2, p. 25, 2021, doi: 10.33365/jtk.v15i2.1162.

R. Adrianto and A. Fahmi, “Penerapan Metode Clustering Dengan Algoritma K-Means Untuk Rekomendasi Pemilihan Jalur Peminatan Sesuai Kemampuan Pada Progam Studi Teknik Informatika - S1 Universitas Dian Nuswantoro,” JOINS (Journal Inf. Syst., vol. 1, no. 2, pp. 101–116, 2019, [Online]. Available: http://publikasi.dinus.ac.id/index.php/joins/article/view/1302

Fina Nasari and S. Surya Darma, “Penerapan K-Means Clustering Pada Data Penerimaan Mahasiswa Baru,” Semin. Nas. Teknol. Inf. dan Multimed. 2015, pp. 73–78, 2015.

M. ISTONINGTYAS, “Penentuan Jurusan ke Perguruan Tinggi Menggunakan Metode Clustering di SMAN 3 Kuala Tungkal,” J. Process., vol. 13, no. 2, 2018, [Online]. Available: http://ejournal.stikom-db.ac.id/index.php/processor/article/view/352

F. Yunita, “Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru,” Sistemasi, vol. 7, no. 3, p. 238, 2018, doi: 10.32520/stmsi.v7i3.388.

Y. R. Sari, A. Sudewa, D. A. Lestari, and T. I. Jaya, “Penerapan Algoritma K-Means Untuk Clustering Data Kemiskinan Provinsi Banten Menggunakan Rapidminer,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 192, 2020, doi: 10.24114/cess.v5i2.18519.

K. Handoko, “Penerapan Data Mining Dalam Meningkatkan Mutu Pembelajaran Pada Instansi Perguruan Tinggi Menggunakan Metode K-Means Clustering (Studi Kasus Di Program Studi Tkj Akademi Komunitas Solok Selatan),” J. Teknol. dan Sist. Inf., vol. 02, no. 03, pp. 31–40, 2016, [Online]. Available: http://teknosi.fti.unand.id/index.php/teknosi/article/view/70

A. Situmorang, A. Arifin, I. Rusilpan, and C. Juliane, “Analisa dan Penerapan Metode Algoritma K-Means Clustering Untuk Mengidentifikasi Rekomendasi Kategori Baru Pada List Movie IMDb,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2171, 2022, doi: 10.30865/mib.v6i4.4729.

I. Mahmud, A. D. Indriyanti, and I. Lazulfa, “Penerapan Algoritma K-Means Clustering Sebagai Strategi Promosi Penerimaan Mahasiswa Baru Pada Universitas Hasyim Asy’ari Jombang,” Inovate, vol. 4, no. 2, pp. 20–27, 2020.

M. Mustofa, “Penerapan Algoritma K-Means Clustering pada Karakter Permainan Multiplayer Online Battle Arena,” J. Inform., vol. 6, no. 2, pp. 246–254, 2019, doi: 10.31311/ji.v6i2.6096.

M. Benri, H. Metisen, and S. Latipa, “Analisis Clustering Menggunakan Metode K-Means Dalam Pengelompokkan Penjualan Produk Pada Swalayan Fadhila,” J. Media Infotama, vol. 11, no. 2, pp. 110–118, 2015, [Online]. Available: https://core.ac.uk/download/pdf/287160954.pdf

M. L. Sibuea and A. Safta, “Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring,” Jurteksi, vol. 4, no. 1, pp. 85–92, 2017, doi: 10.33330/jurteksi.v4i1.28.

J. Hutagalung and F. Sonata, “Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 1187, 2021, doi: 10.30865/mib.v5i3.3113.

Published
2023-12-01
Abstract views: 960 times
Download PDF: 458 times
How to Cite
Anggraini, R., & Sau’da, S. (2023). Stream Clustering for Selection Recommendations Using K-Means Algorithm: A Case Study in the Informatics Study Program. Journal of Information Systems and Informatics, 5(4), 1274-1287. https://doi.org/10.51519/journalisi.v5i4.576