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

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.