Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT

 (*)Enggar Novianto Mail (Universitas Teknologi Yogyakarta, Sleman, Indonesia)
 Arief Hermawan (Universitas Teknologi Yogyakarta, Sleman, Indonesia)
 Donny Avianto (Universitas Teknologi Yogyakarta, Sleman, Indonesia)

(*) Corresponding Author

Submitted: October 16, 2023; Published: February 2, 2024

Abstract

Scholarships are financial assistance provided to individuals, pupils, or scholars to extend their education. These may be provided by government agencies or the colleges themselves to students. One component that ensures quality human resources is formal education. The purpose of scholarships is to help disadvantaged or underprivileged students. Scholarship providers usually give some consideration to the student's level of difficulty, such as parents' salary and number of siblings. Due to the large number of applications for relief scholarships and strict assessment criteria, not all students who apply can be accepted. Scholarship application selection officers often have difficulty determining which students are worthy of receiving a scholarship. While the quota for scholarship recipients for this study program is always limited, applications for student UKT relief scholarships continue to increase every semester. This application came from students with poor economic conditions. To select UKT relief scholarship application documents, you have to consider various criteria and use manual methods which are less effective and require more time to determine the results. This research aims to make a comparison between the K-Nearest Neighbor and Support Vector Machine classification algorithms in determining recipients of UKT relief scholarships for undergraduate students in the Legal Sciences Study Program, Faculty of Law, Sebelas Maret University using the RapidMiner application. The accuracy results obtained using the RapidMiner application that have been carried out, the K-NN method produces an accuracy of 92.92%, while the SVM method produces an accuracy of 85.84%, so the K-NN method is the best method in classification for predicting recipients of UKT relief scholarships for students in the program. Bachelor of Law studies.

Keywords


Classification; SVM; K-NN; Student; Scholarship

Full Text:

PDF


Article Metrics

Abstract view : 72 times
PDF - 11 times

References

H. Hamsir Saleh, "K-Nearest Neighbor Berbasis Seleksi Atribut Chi Square Untuk Klasifikasi Penerima Beasiswa Kurang Mampu," Jurnal SIMETRIS, pp. 1-10, 2023.

H. F. Muhamamd Riyyan, "Perbandingan Algoritme Naive Bayes Dan KNN Terhadap Data Penerimaan Beasiswa (Studi Kasus Lembaga Beasiswa Baznas Jabar)," JIRE (Jurnal Informatika & Rekayasa Elektronika), vol. 5, no. 1, pp. 1-9, 2022.

W. B. P. A. Fakhriyani, "Perbandingan Algoritma Naive Bayes Dan Support Vector Machine Dalam Seleksi Kelulusan Pemberkasan Beasiswa BPP-PPA Fakultas Teknik Universitas Negeri Jakarta," JURNAL PINTER, vol. 2, no. 2, pp. 108-115, 2018.

E. A. H. L. H. W. a. W. S. D Kurniadi, "The Prediction of Scholarship recipients in higher education using k-Nearest neighbor algorithm," in IOP Conference Series: Materials Science and Engineering, Bandung, Indonesia, 2018.

A. K. Putri, "Perilaku Pencarian Informasi Beasiswa Mahasiswa Fakultas Ilmu Budaya Universitas Diponegoro Melalui Media Online," JIPI (Jurnal Ilmu Perpustakaan dan Informasi), vol. 6, no. 2, pp. 259-273, 2021.

S. N. H. S. Okta Misro'i, "Pengaruh Beasiswa KIP Kuliah terhadap Motivasi Beprestasi Mahasiswa Jurusan PIPS FKIP Universitas Riau," Jurnal Pendidikan dan Konseling, vol. 4, no. 6, pp. 6666-6672, 2022.

E. S. N. T. B. K. Nanda Tri Haryati, "Klasifikasi Pemberian Beasiswa Beprestasi Menggunakan Perbandingan Tiga Algoritma," Jurnal TEKNO KOMPAK, vol. 17, no. 1, pp. 54-66, 2023.

N. H. E. S. Caesaradi Rama Raharya, "Penentuan Penerimaan Beasiswa Menggunakan Metode Modified K-Nearest Neighbor," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 11, pp. 4984-4990, 2018.

J. S. and T. R. T. P. Nugraha, "Prediksi Penerima Beasiswa Menggunakan Algoritma C4.5 (Studi Kasus : Universitas Peradaban)," Indonesian Journal of Informatics and Research, vol. 1, no. 1, pp. 36-42, 2020.

R. U. M. D. Ikrimatul Wilda Lorenza, "Klasifikasi Penerima Bantuan Beasiswa Menggunakan Algoritma K-Nearest Neighbour Dengan Seleksi Fitur Backward Elimination," JASIE "Jurnal Aplikasi Sistem Informasi dan Elektronika", vol. 3, no. 1, pp. 26-31, 2021.

I. B. Ari Sudrajat, "Analisis Kinerja Algoritma Support Vector Machine (SVM) Pada Data Seleksi Penerima Beasiswa Menggunakan Particle Swarm Optimization (PSO) (Studi Kasus : Politeknik TEDC Bandung)," Jurnal TEDC, vol. 13, no. 1, pp. 1-11, 2019.

K. H. Pajar Pahrudin, "Penerapan Algoritma K-Nearest Neighbor Uuntuk Klasifikasi Warga Penerima Bantuan Sosial," Building of Informatics, Technology and Science (BITS), vol. 4, no. 3, pp. 1241-1245, 2022.

R. N. Ikhsani and F. F. Abdulloh, "Optimasi SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online," Jurnal Media Informatika Budidarma, vol. 7, no. 4, pp. 1667-1677, 2023.

T. H. R. P. T. A. Saifur Rohman Cholil, "Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa," IJCIT (Indonesian Journal on Computer and Information Technology), vol. 6, no. 2, pp. 118-127, 2021.

M. M. M. T. B. B. E. L. Bosker Sinaga, "Classification Of Student Scholarship Accurate Classification Using The K Nearest Neighbor Algorithm," JURNAL INFOKUM, vol. 10, no. 5, pp. 999-1005, 2022.

N. M. D. R. Mohammad Mastur Alfitri, "Evaluasi Performa Algoritma Naive Bayes Dalam Mengklasifikasi Penerimaan Bantuan Pangan Non Tunai," Jurnal Media Informatika Budidarma, vol. 7, no. 3, pp. 1433-1445, 2023.

S. L. S. R. Baskoro, "Prediksi Penerima Beasiswa dengan Menggunakan Teknik Data Mining di Universitas Muhammadiyah Pringsewu," in Seminar Nasional Hasil Penelitian dan Pengabdian Masyarakat, Lampung, 2021.

A. H. Yunial, "Analisa Perbandingan Algoritma Klasifikasi Support Vector Machine, Decession Tree Dan Naive Bayes," in Prosiding Seminar Nasional Informatika dan Sistem Informasi, Tangerang, Indonesia, 2020.

I. S. E. Z. Hilda Nur Zerlinda, "Klasifikasi Calon Penerima Bidikmisi Dengan Menggunakan Algoritma K-Nearest Neighbor," in Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019 UMT, Tangerang, 2019.

S. S. H. F. N. Dinda Safitri, "Analisis Penggunaan Algoritma Klasifikasi Dalam Prediksi Kelulusan Menggunakan Orange Data Mining," RABIT : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 8, no. 1, pp. 75-81, 2023.

R. A. S. A. R. A. W. E. P. M. N. W. Safitri Linawati, "Perbandingan Algoritma Klasifikasi Naive Bayes Dan SVM Pada Studi Kasus Pemberian Penerima Beasiswa PPA," JURNAL SWABUMI, vol. 8, no. 1, pp. 71-75, 2020.

R. Rian Oktafiani, "Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree Untuk Sistem Rekomendasi Tempat Wisata," Jurnal Nasional Teknologi dan Sistem Informasi, vol. 9, no. 2, pp. 113-121, 2023.

S. Y. Pangestu, Y. Astuti and L. D. Farida, "Algoritma Support Vector Machine Untuk Klasifikasi Sikap Politik Terhadap Partai Politik Indonesia," Jurnal Mantik Penusa, vol. 3, no. 1, pp. 236-241, 2019.

I. E. P. P. A. P. N. W. Agus Budiyantara, "Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Memprediksi Mahasiswa Lulus Tepat Waktu," Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, vol. 5, no. 2, pp. 265-270, 2020.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.