Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score
Abstract
Tuition Assistance is money given specifically to students with the aim of alleviating the problem of paying educational costs for less fortunate students so they can continue their education. With the large number of scholarship applicants on a campus, especially Budidarma University, a computerized information system is needed so that the selection of students who receive tuition assistance can run well. One way that can be implemented is by applying data mining with the K-Means algorithm. From the results of applying the data mining method, it can be concluded that there were 10 students who received tuition assistance who were included in cluster 1 and likewise in cluster 2 there were 10 students who did not receive tuition assistance.
Downloads
References
S. Sukamto, Y. Adriyani, and R. Aulia, “Prediksi Kelompok UKT Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,” JUITA J. Inform., vol. 8, no. 1, p. 121, 2020, doi: 10.30595/juita.v8i1.6267.
F. D. Pratama, I. Zufria, and T. Triase, “Implementasi Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Penerima Program Indonesia Pintar,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 7, no. 1, pp. 77–84, 2022, doi: 10.36341/rabit.v7i1.2217.
R. K. Purba and E. Bu’ulolo, “Implementasi Algoritma K-Medoids dalam Pengelompokan Mahasiswa yang Layak Mendapat Bantuan Uang Kuliah Tunggal,” INSOLOGI J. Sains dan Teknol., vol. 1, no. 2, pp. 79–86, 2022, doi: 10.55123/insologi.v1i2.195.
R. N. Amalda, N. Millah, and I. Fitria, “Implementasi Algoritma C5.0 Dalam Menganalisa Kelayakan Penerima Keringanan Ukt Mahasiswa Itk,” Teorema Teor. dan Ris. Mat., vol. 7, no. 1, p. 101, 2022, doi: 10.25157/teorema.v7i1.6692.
S. Aminah and T. Susanti, “Implementasi Algoritma K-Means Clustering Penerima Bantuan Beasiswa UKT Pada Institut Teknologi Pagar Alam,” J. Ilm. Teknosains, vol. 9, no. 1, 2023, [Online]. Available: https://journal.upgris.ac.id/index.php/JITEK/article/view/15889%0Ahttps://journal.upgris.ac.id/index.php/JITEK/article/viewFile/15889/6936.
F. M. A. Sofyan, A. P. Riyandoro, D. F. Maulana, and J. H. Jaman, “Penerapan Data Mining dengan Algoritma C5.0 Untuk Prediksi Penyakit Stroke,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 6, no. 2, p. 619, 2023, doi: 10.53513/jsk.v6i2.8578.
N. S. Ramadha and W. N. Nursafitri, “Analisis Klasifikasi UKT Mahasiswa Berdasarkan Tingkat Penghasilan Orang Tua Menggunakan Algoritma C4.5,” Invent. J. Inov. dan Tren Pendidik. Teknol. Inf., vol. 2, no. 1, pp. 1–9, 2024, [Online]. Available: https://www.ejournal.tsb.ac.id/index.php/inventor/article/view/1248.
Z. Nurizati, A. Hidayat, D. Vernanda, and T. Hendriawan, “Analisis Kelayakan Penurunan UKT Pada Mahasiswa dengan Menggunakan Metode Decision Tree,” J. TEKNO KOMPAK, vol. 18, no. 1, pp. 90–100, 2024, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknokompak/article/view/3419/1417.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. DAN ILMU Komput., vol. 3, no. 1, pp. 46–56, 2024, [Online]. Available: https://doi.org/10.58602/jima-ilkom.v3i1.26.
P. Marpaung, I. Pebrian, and W. Putri, “Penerapan Data Mining Untuk Pengelompokan Kepadatan Penduduk Kabupaten Deli Serdang Menggunakan Algoritma K-Means,” J. Ilmu Komput. dan Sist. Inf., vol. 6, no. 2, pp. 64–70, 2023.
M. Lasena, R. Aminuddin, and Z. Azhar, “Pembentukan Pola Peminjaman Buku Pada Perpustakaan Dengan Menerapkan Metode CART dan Normalisasi Z-Score,” Build. Informatics, Technol. Sci., vol. 6, no. 1, pp. 314–324, 2024, doi: 10.47065/bits.v6i1.5238.
N. Safitri, D. Kusnandar, and S. Martha, “IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR DENGAN NORMALISASI Z-SCORE DALAM KLASIFIKASI PENERIMA BANTUAN SOSIAL DESA SERUNAI,” Bul. Ilm. Math. Stat. dan Ter., vol. 13, no. 1, pp. 99–106, 2023.
E. Novianto, A. Hermawan, and D. Avianto, “Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT,” J. Media Inform. Budidarma, vol. 8, no. 1, pp. 654–662, 2024, doi: 10.30865/mib.v8i1.6913.
A. Sophia, M. S. Informasi, and U. D. Bangsa, “Penerapan Data Mining dalam Pengelompokan Uang Kuliah Tunggal ( UKT ) Menggunakan Metode K-Means Pada Universitas Jambi,” Manaj. Sist. Inf., vol. 9, no. 1, 2024.
M. R. Kusnaidi, T. Gulo, and S. Aripin, “Penerapan Normalisasi Data Dalam Mengelompokkan Data Mahasiswa Dengan Menggunakan Metode K-Means Untuk Menentukan Prioritas Bantuan Uang Kuliah Tunggal,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 330–338, 2022, doi: 10.47065/josyc.v3i4.2112.
M. Sholeh, D. Andayati, and R. Y. Rachmawati, “Data Mining Model Klasifikasi Menggunakan Algoritma K-Nearest Neighbor Dengan Normalisasi Untuk Prediksi Penyakit Diabetes,” TeIKa, vol. 12, no. 02, pp. 77–87, 2022, doi: 10.36342/teika.v12i02.2911.
Reza Gustrianda and D. I. Mulyana, “Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids,” J. Media Inform. Budidarma, vol. 6, no. 1, pp. 27–34, 2022, doi: 10.30865/mib.v6i1.3294.
N. L. P. P. Dewi, I. N. Purnama, and N. W. Utami, “Penerapan Data Mining Untuk Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: STMIK Primakara),” J. Ilm. Teknol. Inf. Asia, vol. 16, no. 2, p. 105, 2022, doi: 10.32815/jitika.v16i2.761.
A. Ikhwan and N. Aslami, “Implementasi Data Mining untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means,” J. Teknol. Inf., vol. 4, no. 2, pp. 208–217, 2020, doi: 10.36294/jurti.v4i2.2103.
S. Z. H. Rukmana, A. Aziz, and W. Harianto, “Optimasi Algoritma K-Nearest Neighbor (Knn) Dengan Normalisasi Dan Seleksi Fitur Untuk Klasifikasi Penyakit Liver,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 439–445, 2022.
M. L. Radhitya and G. I. Sudipa, “Pendekatan Z-Score Dan Fuzzy Dalam Pengujian Akurasi Peramalan Curah Hujan,” SINTECH (Science Inf. Technol. J., vol. 3, no. 2, pp. 149–156, 2020, doi: 10.31598/sintechjournal.v3i2.567.
I. Permana and F. N. S. Salisah, “Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation,” Indones. J. Inform. Res. Softw. Eng., vol. 2, no. 1, pp. 67–72, 2022, doi: 10.57152/ijirse.v2i1.311.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score
Pages: 1977-1986
Copyright (c) 2024 Yunita Yunita, Muhammad Fahmi, Salmon Salmon

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).