PENGELOMPOKAN ANGGOTA DIVISI HIMPUNAN MAHASISWA JURUSAN PADA UNIVERSITAS “XYZ” DENGAN METODE K-MEANS CLUSTERING
Abstract
The Department of Student Association is a human group formed to have the same goal in building an organization in higher education. So far, HMJ has carried out the process of grouping daily management into each division manually and there is no objective process that causes several candidates for daily management to be placed not according to their abilities. To solve the existing problems, a study was conducted using the K-means Clustering method. The K-Means method is very much needed because it can determine the grouping of candidates for the daily committee using variables that can be a guide for making decisions for candidates who will be selected as daily administrators of the student association majors. In the K-Means calculation, the results of the grouping of candidates for the daily management of the student association majors in each division are obtained according to the criteria determined by the HMJ. Based on the results of the comparison calculations with the original data from the HMJ interview and the results of calculations using K-Means, an accuracy value of 25% was calculated using the accuracy formula in the results and discussion chapter. Therefore, in the further research, weights can be added to the criteria and the data is reproduced so that the accuracy values can be compared in research using the K-Means method.
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References
D. Corry and Y. Indrianingsih, “SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN ANGGOTA HIMPUNAN MAHASISWA JURUSAN TEKNIK INFORMATIKA DENGAN MENGGUNAKAN METODE ELECTRE (Studi Kasus : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta),” [Online].Compiler, 2014. Tersedia : https://ejournals.itda.ac.id/index.php/compiler/article/view/78
Asroni and R. Adrian, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,” J. Ilm. Semesta Tek., [Online]. Vol. 18, no. 1, pp. 76–82, 2015. Tersedia : https://journal.umy.ac.id/index.php/st/article/view/708
I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., [Online]. Vol. 5, no. 1, p. 119, 2019. Tersedia : http://ejournal.uin-suska.ac.id/index.php/RMSI/article/view/7381
Y. Mahena, M. Rusli, and E. Winarso, “Prediksi Harga Emas Dunia Sebagai Pendukung Keputusan Investasi Saham Emas Menggunakan Teknik Data Mining,” Kalbiscentia J. Sains dan Teknol, [Online]. Vol. 2, no. 1, pp. 36–51, 2015. Tersedia : http://research.kalbis.ac.id/Research/Files/Article/Full/LFO7P4HJ6D7O5X587IB41OUPG.pdf
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., [Online]. 2017. Tersedia : https://ejournal.upgrisba.ac.id/index.php/eDikInformatika/article/view/1465
A. Sani, “Penerapan metode k-means clustering pada perusahaan,” J. Ilm. Teknol. Inf., [Online].Vol. 353, pp. 1–7, 2018. Tersedia : https://www.researchgate.net/publication/326849650_PENERAPAN_METODE_K-MEANS_CLUSTERING_PADA_PERUSAHAAN
D. Marlina, N. F. Putri, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT, [Online]. Vol. 4, no. 2, p. 64, 2018. Tersedia : http://ejournal.uin-suska.ac.id/index.php/coreit/article/view/4498/0
A. Ramadhan, Z. Efendi, and M. Mustakim, “Perbandingan K-Means dan Fuzzy C-Means untuk Pengelompokan Data User Knowledge Modeling,” in Seminar Nasional Teknologi Informasi Komunikasi dan Industri, [Online]. 2017, pp. 219–226. Tersedia : http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/3268
D. P. Utomo and B. Purba, “Sistem Pendukung Keputusan Penilaian Kinerja Tenaga Kependidikan (TENDIK) Dengan Menggunakan Metode SMARTER,” J. Komtika (Komputasi dan Inform., [Online]. Vol. 5, no. 2, pp. 140–152, 2021. Tersedia : https://journal.unimma.ac.id/index.php/komtika/article/view/5619
M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,” J. Inform. J. Pengemb. IT, [Online]. 2019, doi: 10.30591/jpit.v4i1.1253. Tersedia : https://ejournal.poltektegal.ac.id/index.php/informatika/article/view/1253/0
A. Asroni, H. Fitri, and E. Prasetyo, “Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik),” Semesta Tek., [Online]. 2018, doi: 10.18196/st.211211. Tersedia : https://journal.umy.ac.id/index.php/st/article/view/2610
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