PENGELOMPOKKAN DATA MAHASISWA MENGGUNAKAN CLUSTERING UNTUK OPTIMALISASI PENERIMAAN MAHASISWA BARU

Authors

  • Diana Yusuf Institut Teknologi dan Bisnis Ahmad Dahlan
  • Elliya Sestri Institut Teknologi dan Bisnis Ahmad Dahlan
  • Fahrul Razi Institut Teknologi dan Bisnis Ahmad Dahlan

DOI:

https://doi.org/10.31000/jika.v8i4.12637

Abstract

Salah satu tahapan penting dalam pengelolaan perguruan tinggi yakni proses penerimaan mahasiswa baru, dimana proses ini akan mempengaruhi kualitas dan kuantitas mahasiswa yang diterima di perguruan tinggi. Mengoptimalkan proses ini memerlukan pendekatan yang efektif untuk menganalisis data potensi mahasiswa. Dimana akan dilakukan pengelompokkan data mahasiswa menggunakan algoritma clustering K-Means untuk menemukan pola dan karakteristik yang dapat mengoptimalkan penerimaan mahasiswa baru. Penerapan algoritma K-Means vabel-variabel seperti program studi, IPK, kelurahan, kota, provinsi, dan jenis sekolah. Hasil pengelompokkan diharapkan dapat memberikan wawasan lebih dalam mengenai segementasi calon mahasiswa, sehingga perguruan tinggi dapat menyusun strategi penerimaan yang lebih tepat sasaran. Diharapkan dapat memberikan dasar bagi pengambilan keputusan yang lebih berbasis data untuk meningkatkan kualitas penerimaan mahasiswa pada masa mendatang.

References

Afiasari, N., Suarna, N., & Rahaningsi, N. (2023). Implementasi Data Mining Transaksi Penjualan Menggunakan Algoritma Clustering dengan Metode K-Means. Jurnal SAINTEKOM, 13(1), 100–110. https://doi.org/10.33020/saintekom.v13i1.402

Alhapizi, M. R., Nasir, M., & Effendy, I. (2020). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru Universitas Bina Darma Palembang. Journal of Software Engineering Ampera, 1(1), 1–14. https://doi.org/10.51519/journalsea.v1i1.10

Dhewayani, F. N., Amelia, D., Alifah, D. N., Sari, B. N., & Jajuli, M. (2022). Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM. Jurnal Teknologi Dan Informasi, 12(1), 64–77. https://doi.org/10.34010/jati.v12i1.6674

Haris Kurniawan, Sarjon Defit, & Sumijan. (2020). Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal. Journal of Applied Computer Science and Technology, 1(2), 80–89. https://doi.org/10.52158/jacost.v1i2.102

Hendrastuty, N. (2024). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa. Jurnal Ilmiah Informatika Dan Ilmu Komputer (JIMA-ILKOM), 3(1), 46–56.

Muzakir, A., & Wulandari, R. A. (2016). Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree. Scientific Journal of Informatics, 3(1), 19–26. https://doi.org/10.15294/sji.v3i1.4610

Rafi Nahjan, M., Nono Heryana, & Apriade Voutama. (2023). IMPLEMENTASI RAPIDMINER DENGAN METODE CLUSTERING K-MEANS UNTUK ANALISA PENJUALAN PADA TOKO OJ CELL. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 101–104. https://doi.org/10.36040/jati.v7i1.6094

Sari, Y. P., Primajaya, A., & Irawan, A. S. Y. (2020). Implementasi Algoritma K-Means untuk Clustering Penyebaran Tuberkulosis di Kabupaten Karawang. INOVTEK Polbeng - Seri Informatika, 5(2), 229. https://doi.org/10.35314/isi.v5i2.1457

Sugianto, C. A., Rahayu, A. H., & Gusman, A. (2020). Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2(2), 39–44. https://doi.org/10.47292/joint.v2i2.30

Sulistiyawati, A., & Supriyanto, E. (2021). Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan. Jurnal Tekno Kompak, 15(2), 25. https://doi.org/10.33365/jtk.v15i2.1162

Veronica, M., & Effendi, H. (2023). Clustering Tingkat Kedisiplinan Pegawai Pada Pengadilan Tinggi Palembang Menggunakan Algoritma K-Means. Prosiding CORISINDO 2023.

Yudhistira, A., & Andika, R. (2023). Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering. Journal of Artificial Intelligence and Technology Information (JAITI), 1(1), 20–28. https://doi.org/10.58602/jaiti.v1i1.22

Yusuf, D., & Sestri, E. (2020). Metode Decision Tree Dalam Klasifikasi Kredit Pada Nasabah PT Bank Perkreditan Rakyat (Studi Kasus : PT BPR Lubuk Raya Mandiri). Jurnal Sistem Informasi (JUSIN), 1(1), 21–28. https://doi.org/10.32546/jusin.v1i1.855

Yusuf, D., Sestri, E., & Razi, F. (2023). Implementasi Teknik Clustering Untuk Pengelompokan Mobil Bekas Berdasarkan Grade Pada Mobi Auto. J-SISKO TECH (Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD), 6(2), 307. https://doi.org/10.53513/jsk.v6i2.8352

Zai, C. (2022). Implementasi Data Mining Sebagai Pengolahan Data. Jurnal Portal Data, 2(3)

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Published

2024-10-31

Issue

Vol. 8 No. 4 (2024): JIKA (Jurnal Informatika)

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