Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time

Main Article Content

Rizki Muliono Juanda Hakim Lubis Nurul Khairina
Corresponding Author:
Nurul Khairina | nurulkhairina27@gmail.com

Copyright (C):
Rizki Muliono, Juanda Hakim Lubis, Nurul Khairina

Abstract

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.

Keyword: prediction, graduation time, k nearest neighbor

Downloads

Download data is not yet available.

Article Details

How to Cite
MULIONO, Rizki; LUBIS, Juanda Hakim; KHAIRINA, Nurul. Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time. SinkrOn, [S.l.], v. 4, n. 2, p. 42-46, mar. 2020. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10480>. Date accessed: 09 aug. 2020. doi: https://doi.org/10.33395/sinkron.v4i2.10480.
Section
Articles
* Abstract viewed = 0 times PDF downloaded = 0 times *

References

Agrawal, R. (2019). Integrated Parallel K-Nearest Neighbor Algorithm. In Smart Intelligent Computing and Applications (p. 479). Springer Singapore. https://doi.org/10.1007/978-981-13-1921-1

Atma, Y. D., & Setyanto, A. (2018). Perbandingan Algoritma C4.5 dan K-NN dalam Identifikasi Mahasiswa Berpotensi Drop Out. Metik Jurnal, 2(2), 31–37.

Czumaj, A., & Sohler, C. (2020). Sublinear time approximation of the cost of a metric k -nearest. In Society for Industrial and Applied Mathematics (pp. 2973–2992).

Gou, J., Ma, H., Ou, W., Zeng, S., Rao, Y., & Yang, H. (2019). A Generalized Mean Distance-Based K-Nearest Neighbor Classifier. Expert Systems with Applications, 115, 3–24. https://doi.org/10.1016/j.eswa.2018.08.021

Hakim, L. A. R., Rizal, A. A., & Ratnasari, D. (2019). Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN). JTIM : Jurnal Teknologi Informasi Dan Multimedia, 1(1), 30–36. https://doi.org/10.35746/jtim.v1i1.11

Muliono, R. (2017). Implementasi Algoritma Apriori Pada Data Benchmark Kosarak Dan Mushrooms. Journal of Informatics and Telecommunication Engineering, 1(1), 34–41.

Muliono, R., Muhathir, Khairina, N., & Harahap, M. K. (2019). Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets. In 1st International Conference of SNIKOM 2018 (pp. 1–5). https://doi.org/10.1088/1742-6596/1361/1/012036

Muliono, R., & Sembiring, Z. (2019). Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen. CESS (Journal Of Computer Engineering, System And Science), 4(2), 272–279.

Nikmatun, I. A., & Waspada, I. (2019). Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor. Jurnal Simetris, 10(2), 421–432.

Novianti, A. G., & Prasetyo, D. (2017). Penerapan Algoritma K-Nearest Neighbor (K-NN) untuk Prediksi Waktu Kelulusan Mahasiswa. In Seminar Nasional APTIKOM(SEMNASTIKOM) (pp. 108–113).

Prasetyo, A., Kusrini, & Arief, M. R. (2019). Penerapan Algoritma K Nearest Neighbor untuk Rekomendasi Minat Konsentrasi di Program Studi Teknik Informatika Universtias PGRI Yogyakarta. Informasi Interaktif, 4(1), 1–6.

Purwanto, E., Kusrini, & Sudarmawan. (2019). Prediksi Kelulusan Tepat Waktu Menggunakan Metode C4 . 5 DAN K - NN (Studi Kasus : Mahasiswa Program Studi S1 Ilmu Farmasi , Fakultas Universitas Muhammadiyah Purwokerto ). TECHNO, 20(2), 131–142.

Rahmatullah, S., & Utami, E. (2019). Prediksi Tingkat Kelulusan Tepat Waktu dengan Metode Naive Bayes dan K-Nearest Neighbor. Jurnal Informasi Dan Komputer, 7(1), 7–16.

Rohman, A., & Rochcham, M. (2019). Komparasi Metode Klasifikasi Data Mining untuk Prediksi Kelulusan Mahasiswa. Jurnal Neo Teknika, 5(1), 23–31.

Tang, B., He, H., & Zhang, S. (2020). MCENN: A Variant of Extended Nearest Neighbor Method for Pattern Recognition. Pattern Recognition Letters, 1–10. https://doi.org/10.1016/j.patrec.2020.01.015

Wang, Y., Wang, R., Li, D., Adu-Gyamfi, D., Tian, K., & Zhu, Y. (2019). Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbor Algorithm. International Journal of Theoretical Physics, 58(7), 2331–2340. https://doi.org/10.1007/s10773-019-04124-5