IMPLEMENTASI DATA MINING UNTUK MENENTUKAN MINAT SISWA DALAM MENENTUKAN JURUSAN PADA PERGURUAN TINGGI
Dublin Core
Title
IMPLEMENTASI DATA MINING UNTUK MENENTUKAN MINAT SISWA DALAM MENENTUKAN JURUSAN PADA PERGURUAN TINGGI
Subject
Data mining, Minat Siswa
Description
Class XII high school students are students who occupy a period of formal education before entering lectures, students who are in the teenage age range where at this age they have to make decisions for their future. In making these decisions, adolescents are often accompanied by confusion, uncertainty, and even stress so that making decisions that result in regrets in the future. Every year there are many vocational high school students who want to go to a college but do not know what major they want and apply in the world of work according to their talents and interests, so there are still many students who make decisions that are not in accordance with their interests and talents. make decisions based on the opinions of parents, friends or others. For this reason, a model is needed to classify these problems. In this study, three classification algorithms were used: decision tree, nave Bayes, and k-nearest neighbor with data mining techniques to find patterns from the model used, the results of this study are expected to help students determine the majors to be taken in lectures. From the results of this research test, the factor that most influenceserrors in majoring in college is the variable of majoring based on (self/friends/parents), and of the three algorithms used, the decision tree algorithm is the best algorithm with a high level of accuracy 75.38%
Creator
Saeful Bahri1(
Source
https://ojs.itb-ad.ac.id/index.php/JUSIN/article/view/1644/365
Publisher
ITB Ahmad Dahlan, Jakarta
Date
2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Saeful Bahri1(, “IMPLEMENTASI DATA MINING UNTUK MENENTUKAN MINAT SISWA DALAM MENENTUKAN JURUSAN PADA PERGURUAN TINGGI,” Repository Horizon University Indonesia, accessed January 22, 2025, https://repository.horizon.ac.id/items/show/7443.