Implementasi Algoritma K-Nearest Neighbors Pada Penentuan Jurusan Siswa

 (*)M. Daffa Alkhussayid Mail (Universitas Binadarma, Palembang, Indonesia)
 Ferdiansyah Ferdiansyah (Universitas Binadarma, Palembang, Indonesia)

(*) Corresponding Author

Submitted: August 28, 2022; Published: September 30, 2022

Abstract

SMA Negeri 8 Palembang has two majors, namely science and social studies. Determination of majors is done when in class X to determine the majors of the student. This research was conducted because the teacher had difficulty in determining the majors at SMA Negeri 8 Palembang. In the research case, the researcher uses the classification with the k-Nearest Neighbors algorithm, and the Euclidean distance measurement method to predict the students in determining the majors that will be taken by students. The source of the data for this research is the report card scores for the X grade students of SMA Negeri 8 Palembang, and the data for this research are 335 data on the grade X students of SMA Negeri 8 Palembang. The data collection was taken from the grade X class report cards, namely mathematics, physics, biology, English, Indonesian, history, geography, economics, and psychological test scores. In determining the majors, students in science and social studies get the average score of all subjects and the psychological test scores produced by these students to enter the science department with an average score of 80, math score 78, physics 78, biology 78, and psychological test 80. If students get an average score below 80, it will be predicted to enter social studies, to enter the social studies department with a minimum score of 70 geography subjects, 70 economics, 70 history, and 70 psychological test scores. The results obtained in this study used the K method. -Nearest Neighbors based on training data obtained from 335 student data, 101 classified social studies class according to predictions, 10 data predicted social studies, but data declared natural science, 2 science prediction data and, 222 data according to natural science predictions, and the accuracy got 96% and the results of observations using the Website using K-NN show the same data results obtained through an accuracy of 96%.

Keywords


SMA Negeri 8 Palembang; K-Nearest Neighbors; Major Prediction; Machine Learning

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