Algoritma K-Medoids Untuk Menentukan Calon Mahasiswa Yang Layak Mendapatkan Beasiswa Bidikmisi di Universitas Budi Darma

Authors

  • Efori Buulolo Universitas Budi Darma, Medan
  • Rian Syahputra Universitas Budi Darma, Medan
  • Alwin Fau Universitas Budi Darma, Medan

DOI:

https://doi.org/10.30865/mib.v4i3.2240

Keywords:

Students, Scholarships, Bidikmisi, Algorithms, K-Medoids

Abstract

Bidikmisi scholarship is a government program to help prospective new students who are academically capable and economically incapable. The Bidikmisi scholarship is a form of tuition assistance and living expenses. Starting in 2018 Budi Darma University began accepting new students through the bidikmisi scholarship path, admission of new students through the bidikmisi path must meet the requirements set by the government. Determination of whether or not a prospective new student is a recipient of bidikmisi based on report cards, school performance, results of selection tests and interviews. During this time the organizers and managers of bidikmisi at Budi Darma University have had difficulty determining prospective students who are truly eligible to receive bidikmisi scholarships in addition to the very limited quota and the large number of prospective students receiving bidikmisi scholarships as well as the value of each criteria for prospective students receiving bidikmisi which is almost the same or similar to one another. To make it easier to determine prospective students receiving the Bidikmisi scholarship, the K-Medoids algorithm is used. K-Medoids algorithm is one of the algorithms in data mining to group data based on the closest criteria value

Author Biographies

Efori Buulolo, Universitas Budi Darma, Medan

Fakultas Ilmu Komputer dan Teknologi Informasi

Rian Syahputra, Universitas Budi Darma, Medan

Fakultas Ilmu Komputer dan Teknologi Informasi

Alwin Fau, Universitas Budi Darma, Medan

Fakultas Ilmu Komputer dan Teknologi Informasi

References

Ditjen Belmawa Kemenristekdikti, Penduan Pendaftaran Beasiswa Bidikmisi 2019. Jakarta: Ristekdikti, 2019.

E. Buulolo, “Implementasi Algoritma Apriori Pada Sistem Persediaan Obat ( Studi Kasus : Apotik Rumah Sakit Estomihi Medan ),†Pelita Inform. Budi Darma, vol. 4, no. Agustus 2013, pp. 71–83, 2013.

A. Widayanti, “Analisis Kluster untuk Mengelompokkan Performansi Mahasiswa Fakultas Ilmu Terapan Ditinjau dari Bidang Akademik dan Non Akademik,†J. Teknol. Inf., vol. 1, no. 6, pp. 229–231, 2013.

A. Syahrin, “Implementasi algoritma k-means untuk klasterisasi mahasiswa berdasarkan prediksi waktu kelulusan skripsi,†UPN “Veteran†Jatim, vol. 1–23, 2013.

D. F. Pramesti, Lahan, M. Tanzil Furqon, and C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 9, pp. 723–732, 2017.

W. A. Triyanto, “ALGORITMA K-MEDOIDS UNTUK PENENTUAN STRATEGI PEMASARAN PRODUK,†Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., 2015.

J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,†J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.

A. Bhat, “K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Recognition,†Int. J. Soft Comput. Math. Control, 2014.

P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,†in Physics Procedia, 2016.

E. Buulolo, Data Mining Untuk Perguruan Tinggi. Yogyakarta: deepublish, 2020.

I. Kamila et al., “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,†vol. 5, no. 1, pp. 119–125, 2019.

Aprilla Dennis, “Belajar Data Mining dengan RapidMiner,†Innov. Knowl. Manag. Bus. Glob. Theory Pract. Vols 1 2, 2013.

V. Kotu and B. Deshpande, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. 2014.

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Published

2020-07-20

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