Sistem Penentuan Konsentrasi Jurusan Bagi Mahasiswa Informatika Menggunakan Metode K-Means Di Institut Asia Malang

Puji Subekti; Titania Dwi Andini; Mufidatul Islamiyah

doi:10.34010/jamika.v12i1.6452

Puji Subekti Institut Teknologi dan Bisnis Asia
Titania Dwi Andini Institut Teknologi dan Bisnis Asia Malang
Mufidatul Islamiyah Institut Teknologi dan Bisnis Asia

DOI: https://doi.org/10.34010/jamika.v12i1.6452

Keywords: data mining, the concentration of major, K-Means, clustering

Abstract

Specialization in the concentration of a major is a student's focus on a particular field of study according to his interests. The purpose of specialization is to focus students more on the knowledge gained from previous courses so that they can be more focused. In the Informatics Engineering study program at the Institute of Asia Malang, there are 3 concentrations, namely: Intelligent Systems; Multimedia and Games and, Computer Network Administration. The absence of a system that helps students choose the concentration of majors is quite difficult for students to know their academic abilities. By looking at these problems, this study aims to build a decision-making system in choosing the concentration of majors using the K-Means Clustering method. Where the data value of semester 1 to semester 4 students is used as a variable in the calculation of the k-means clustering method which is implemented using the PHP and MySQL programming languages. Based on the test results, it was found that in cluster 1 there were 3 students who entered the intelligent system concentration. In the second cluster, there are 20 students who are in the Multimedia and Game concentration, in the third cluster there are 56 students who are in the network concentration.

References

A. P. Fadillah and M. R. Fachrizal, “Sistem Pendukung Keputusan Untuk Pemilihan Konsentrasi Mata Kuliah (Studi Kasus Program Studi Sistem Informasi Unikom),” J. Manaj. Inform., vol. 8, no. 2, 2018, doi: 10.34010/jamika.v8i2.1029.

Hartatik, “Klasifikasi Konsentrasi Penjurusan Mahasiswa Universitas Amikom Yogyakarta,” Intecno J., vol. 1, pp. 15–18, 2019.

F. K. Wattimury and J. R. Utara, “PENENTUAN PEMINATAN MAHASISWA PRODI INFORMATIKA DI UNIVERSITAS AMIKOM YOGYAKARTA MENGGUNAKAN SVM,” vol. 1, no. 4, pp. 1–4, 2019.

Y. Religia and A. Susanto, “PENERAPAN ALGORITMA K-MEANS DALAM PENGELOMPOKAN CURAH HUJAN DI DAERAH JABODETABEK,” vol. 8, no. 2, pp. 217–224, 2018.

A. U. Fitriyadi, “Algoritma K-Means dan K-Medoids Analisis Algoritma K-Means dan K-Medoids Untuk Clustering Data Kinerja Karyawan Pada Perusahaan Perumahan Nasional,” Kilat, vol. 10, no. 1, pp. 157–168, 2021, doi: 10.33322/kilat.v10i1.1174.

S. Setiawan, “Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 6, no. 1, pp. 41–48, 2018, doi: 10.33558/piksel.v6i1.1398.

B. Harahap, “Penerapan Algoritma K-Means Untuk Menentukan Bahan Bangunan Laris (Studi Kasus Pada UD. Toko Bangunan YD Indarung),” Reg. Dev. Ind. Heal. Sci. Technol. Art Life, pp. 394–403, 2019.

W. A. Wahyuni and S. Saepudin, “Penerapan Data Mining Clustering Untuk Mengelompokkan Berbagai Jenis Merk Mesin Cuci,” in Seminar Nasional Sistem …, 2021, pp. 306–313, [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/35%0Ahttps://sismatik.nusaputra.ac.id/index.php/sismatik/article/download/35/31.

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.

I. Sumadikarta and E. Abeiza, “PENERAPAN ALGORITMA K-MEANS PADA DATA MINING UNTUK MEMILIH PRODUK DAN PELANGGAN POTENSIAL (Studi Kasus : PT Mega Arvia Utama),” J. Satya Inform., vol. 1, no. 1, pp. 1–12, 2018.

Supangat and A. R. Amna, “Pemanfaatan Fitur Analisis Data Menggunakan K-Means Cluster Dalam Point of Sales (POS),” Teknika, vol. 8, no. 2, pp. 97–102, 2019, doi: 10.34148/teknika.v8i2.157.

Sadewo, “Algoritma K-Means Dalam Mengelompokkan Desa / Kelurahan Menurut Keberadaan Keluarga Pengguna Listrik dan Sumber Penerangan Jalan Utama Berdasarkan Provinsi,” Semin. Nas. Teknol. Komput. Sains SAINTEKS 2019, pp. 754–761, 2018.

L. Zahrotun and U. Linarti, “Analisis Data Mining Pengelompokan Data Warga Menggunakan Metode Statistik K-Means,” J. Teknol. AKPRIND, vol. 9, no. 1, pp. 18–23, 2016, [Online]. Available: https://ejournal.akprind.ac.id/index.php/jurtek/article/view/1134/917.

B. Parlambang and Fauziah, “Implementasi Algoritma K-Means Dalam Proses Penilaian Kuesioner Kepada Dosen Guna Mendukung Kepuasan Mahasiswa Terhadap Dosen,” J. Ilm. Teknol. dan Rekayasa, vol. 25, no. 2, pp. 161–173, 2020, doi: 10.35760/tr.2020.v25i2.2719.

N. W. Saputra, A. P. Wibawa, U. Pujianto, and P. Anugrah, “Pengembangan Bahan Ajar Data Mining Menggunakan Four-D Model dalam Kerangka Kerja CDIO,” Belantika Pendidik., vol. 3, no. 2, pp. 48–58, 2020, doi: 10.47213/bp.v3i2.92.

B. K. Khotimah et al., “Optimasi Bobot K-Means Clustering Untuk Mengatasi Optimization Weight of K-Means Clustering,” vol. 8, no. 4, 2021, doi: 10.25126/jtiik.202184912.

Jasmir, “Implementasi Teknik Data Cleaning dan Teknik Roughset pada Data Tidak Lengkap dalam Data Mining,” Semin. Nas. APTIKOM, pp. 99–106, 2016.

F. Marisa, “Educational Data Mining (Konsep dan Penerapan),” J. Teknol. Inf., vol. 4, no. 2, pp. 91–93, 2013.

I. Menarianti, “Klasifikasi data mining dalam menentukan pemberian kredit bagi nasabah koperasi,” J. Ilm. Teknosains, vol. 1, no. 1, pp. 1–10, 2015, [Online]. Available: http://e-jurnal.upgrismg.ac.id/index.php/JITEK/article/view/836.

A. Ilham, “Level Data Untuk Menangani Data Kelas Tidak Seimbang,” J. Ilm. Ilmu Komput., vol. 3, no. 1, pp. 9–14, 2017.