Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Teika
Edwin J Solaiman
Fakultas Teknologi Informasi, Universitas Advent Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penerapan Algoritma Decision Tree C4.5 Untuk Klasifikasi Mahasiswa Berpotensi Drop out Di Universitas Advent Indonesia Daniel Sinaga; Edwin J Solaiman; Fergie Joanda Kaunang
TeIKa Vol 11 No 2 (2021): TeIKa: Oktober 2021
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/teika.v11i2.2613

Abstract

One of the factors that determine the quality of higher education is the percentage of students' ability to complete their studies on time. At present, the problem of student failure and the factors causing it to be an interesting topic to research. Higher education institutions need to detect the behavior of students who have an "undesirable" status so that the factors causing their failure can be identified. Based on the description above, it is necessary to analyze student data such as Gender, Age, Religion, Residence, Social Studies, Discipline, and Debt, based on student data that is as much as 98 data so that it can be used in data mining processing. Where data mining is used to dig and get information from large amounts of data. One of the data mining methods is data classification. By using the classification method with the concept of the C4.5 Decision Tree Algorithm, it produces an accuracy of 90.00%, the result of precision is 87.50, and the result of the recall is 100%. It is hoped that it can increase the desire of the University or Higher Education Institution to provide good thoughts, views, and new policies to students who have problems in lectures, in other words maximizing students in an effort to increase the percentage of student interest in college.
Co-Authors Daniel Sinaga Kaunang, Fergie Joanda