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All Journal CCIT (Creative Communication and Innovative Technology) Journal
Kartika Rahmayani
Universitas Sriwijaya
Computer Science & IT

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Implementation of K-Nearest Neighbor Method and Weighted Product Method in Determining High School Majors Kartika Rahmayani; Yunita Yunita; Kanda Januar
CCIT Journal Vol 15 No 2 (2022): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.893 KB) | DOI: 10.33050/ccit.v15i2.2116

Abstract

High school education in Indonesia is divided into several majors that have been determined by the ministry of education. The Major will also have an influence on students when they will continue their education to the university level. Therefore, students must be placed in majors that are in accordance with their abilities and desires so that they can complete their education well. To assist the school in providing advice on the division of student majors and provide more accurate results, the authors conducted research using the K-Nearest Neighbor method which will classify students so that they are classified into students majoring in science and social studies. K-Nearest Neighbor is used because it can classify student testing data in the case of class 2020 by adapting solutions from student training data in cases of class 2019 based on the data they have. Furthermore, so that student data that has been classified can be sorted based on the best value so that class division can be carried out according to the results of the sequence of students in each majors, the Weighted Product method is used. The Weighted Product method sorts student data based on criteria values that have different weight values. The results in this study provide the highest accuracy value for the K-Nearest Neighbor method using the k value configuration of 88% and the accuracy value 84% for using the Weighted Product method.
Co-Authors Kanda Januar Yunita Yunita