Klustering Data Mahasiswa Menggunakan Metode K-Means Sebagai Acuan dalam Penentuan Uang Kuliah Tunggal (UKT) Mahasiswa
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Abstract
Determining Uang Kuliah Tunggal/UKT for new students is important in Penerimaan Mahasiswa Baru/PMB process after PMB selection process. The determination of UKT groups by The PMB committee at Politeknik Negeri Cilacap is carried out one by one by looking at the economic data of new students. This condition has become a special problem due to the increase in PMB quotas in the PNC, so it requires alternative solutions that can be used as one of the benchmarks in the determination of a new student UKT group in PNC. The researchers used clustering with features that represent the economic conditions of new students with the K-means method to provide alternative solutions. The result of using the K-Means method in clustering, yielding a performance value for the number of clusters 8 of 1669,283, with the highest number of cluster members in cluster members in cluster 4 being 72 out of 275 data. The Elbow method test results to determine the best number of clusters resulting in 4 cluster with a performance value of 2462,003.
References
Kemendikbud RI, “Peraturan Menteri Pendidikan Dan Kebudayaan Republik Indonesia Nomor 25 Tahun 2020,” Kementeri. Pendidik. dan Kebud. RI, pp. 1–76, 2020.
and B. W. Y. Dai, G. Yuan, Z. Yang, “K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection,” Sci Progr., vol. 2021, 2021, doi: 10.1155/2021/9972589.
A. Wirawan and D. Prasetyawan, “Analisis cluster data latar belakang ekonomi mahasiswa untuk rekomendasi penentuan uang kuliah tunggal dengan model K-Modes,” INFOTECH J. Inform. Teknol., vol. 4, no. 2, pp. 234–246, 2023, doi: 10.37373/infotech.v4i2.898.
Z. Nabila, A. R. Isnain, Permata, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
N. Dwitri, J. A. Tampubolon, S. Prayoga, F. I. R.H Zer, and D. Hartama, “Penerapan Algoritma K-Means Dalam Menentukan Tingkat Penyebaran Pandemi Covid-19 Di Indonesia,” J. Teknol. Inf., vol. 4, no. 1, pp. 128–132, 2020, doi: 10.36294/jurti.v4i1.1266.
A. Fatkhudin, A. Khambali, F. A. Artanto, and N. A. P. Zade, “Implementasi Algoritma Clustering K-Means Dalam Pengelompokan Mahasiswa Studi Kasus (Prodi Manajemen Informatika),” J. Minfo Polgan, vol. 12, no. 2, pp. 777–783, 2023, [Online]. Available: https://jurnal.polgan.ac.id/index.php/jmp/article/view/12494/1682
Haris Kurniawan, Sarjon Defit, and Sumijan, “Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal,” J. Appl. Comput. Sci. Technol., vol. 1, no. 2, pp. 80–89, 2020, doi: 10.52158/jacost.v1i2.102.
R. S. Wahono, Data Mining Data mining, vol. 2, no. January 2013. 2023. [Online]. Available: https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part
J. Ha, M. Kambe, and J. Pe, Data Mining: Concepts and Techniques. 2011. doi: 10.1016/C2009-0-61819-5.
D. N. Anjar Wanto, Muhammad Noor Hasan Siregar, Agus Perdana Windarto, Dedy Hartama, Ni Luh Wiwik Sri Rahayu Ginantra, M. R. L. S. V. D. Edi Surya Negara, and C. Prianto, Data Mining Algoritma dan Implementasi, 1st ed. 2020.
and M. D. D. Tambun, S. Fauziah, “Pengelompokan Penerimaan Mahasiswa Baru Dengan Algoritma K-Means Untuk Meningkatkan Potensi Pemasaran,” Bull. Inf. Technol., vol. 4, pp. 294–298, 2023, doi: 10.47065/bit.v3i1.
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.
and Y. Y. S. Dewi, S. Defit, “Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means,” J. Sistim Inf. dan Teknol., vol. 3, pp. 28–33, 2021, doi: 10.37034/jsisfotek.v3i1.40.
Narwati, “Pengelompokan Mahasiswa Menggunakan Algoritma K-Means,” J. Din. Informormatik, vol. 2, pp. 1–7, 2010.
D. Gianyar, “PENERAPAN ALGORITMA K-MEANS CLUSTERING,” vol. 8, no. 2, pp. 1543–1547, 2024.
N. P. M. N. D. and I. B. G. Dwidsamara, “Implementation of K-Modes Algorithm for Clustering of Stress Causes in University Students,” J. Elektron. Ilmu Komput. Udayana, vol. 9 No. 3, pp. 419–427, 2021.
A. P. Riani, A. Voutama, and T. Ridwan, “Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Penerapan K-Means Clustering Dalam Pengelompokan Hasil Belajar Peserta Didik Dengan Metode Elbow Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD,” vol. 6, pp. 164–172, 2023.
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