Article Per Year (5 Year)
p-Index From 2020 - 2025
1.456
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pendidikan Teknologi Informasi (JUKANTI)
Adi Nugroho
Universitas Kristen Satya Wacana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)

 1 
Analisis Prediksi Mahasiswa Terhadap Kelulusan Tepat Waktu Menggunakan Metode Data Mining Decision Tree (Studi Kasus: FTI UKSW) Imelda Ruwae Lutunani; Adi Nugroho
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.781

Abstract

In general, students have the responsibility to complete their studies at a university. For students of the Satya Wacana Christian University Faculty of Information Technology, which every year there are more and more students, the world of work is currently required to become someone who masters the field of technology. In addition, as a student, there are many things that must be done to complete studies by participating in activities on campus, organizations, and being active in the teaching and learning process so that they can complete their studies on time. In this study, a predictive analysis of SWCU FTI students will be conducted on timely graduation using the decision tree data mining method. which will see students who graduate on time and graduate late using the decision tree algorithm which is a decision tree algorithm that has a high level of accuracy in large amounts of data. In this study, the decision tree algorithm was used to run 983 sample data, resulting in a match accuracy of 91.25%. This means that it is very good and effective in predicting student graduation
Co-Authors Agustho Isai Ariya Dwika Cahyono Chrisandy Noel Siruru Daniel Setyo Cahyo Utomo Demira Intan Suranda Gracia Radiena Imelda Ruwae Lutunani Stefan Agustinus Timothy Garry Van Solang Windy Kusumawati Suhet