Authors:

I Made Ryan Prana Dhita, Gst. Ayu Vida Mastrika Giri

Abstract:

“One of the factors that influences students’ graduation rates is their performance in learning. Predicting graduation rates based on student performance has the benefit of analyzing academically underperforming students and providing support to students who face difficulties in the learning process. There are several factors to consider in predicting students’ graduation rates, such as academic grades, attitudes, and social factors. However, these factors alone are not sufficient to effectively predict students’ performance, and educators also struggle to identify which factors affect students’ performance.To predict the performance of school students, the K-Nearest Neighbor (KNN) method is utilized. The K-Nearest Neighbor method is often used in classifying students’ performance due to its simplicity and ability to produce significant and competitive results. In this research, the prediction of students’ graduation rates is carried out using the KNN method.The results of implementing the prediction of students’ performance using the KNN method can serve as a reference for students to improve their achievements and assist educators in considering future teaching materials. Keywords: KNN, K-Nearest Neighbor, Students Performance, Student”

Keywords

KNN, K-Nearest Neighbor, Students Performance, Student

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PDF:

https://jurnal.harianregional.com/jnatia/full-102504

Published

2023-07-17

How To Cite

DHITA, I Made Ryan Prana; MASTRIKA GIRI, Gst. Ayu Vida. Implementasi Algoritma KNN untuk Memprediksi Performa Siswa Sekolah.Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 1, n. 3, p. 819-826, july 2023. ISSN 3032-1948. Available at: https://jurnal.harianregional.com/jnatia/id-102504. Date accessed: 08 Jul. 2024.

Citation Format

ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023

Section

Articles

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