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Implementation of data mining to predict student graduation using C4.5 algorithm method

  • Dewi Anggraeni
  • , Rizaldi*
  • , Akmal Nasution
  • , Abdul Kholiq
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Students graduating on time are an essential indicator for a higher education institution in supporting campus accreditation. Several factors cause students to graduate on time, namely the origin of the previous student's school and student interest. This study aims to predict student graduation based on the head of the last student's school and student interest so that higher education institutions can get the basis for decisions that will be taken in the future. The method used in analyzing student data and supporting criteria for predicting student graduation is the C4.5 algorithm. Then for the decision tree classifier, this research uses data mining.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
Editors Yerizon, Paulo Canas Rodrigues, Goh Khang Wen, Devni Prima Sari, Fridgo Tasman, Ronal Rifandi, Nurul Afifah Rusyda
PublisherAmerican Institute of Physics
Edition1
ISBN (Electronic)9780735449091
DOIs
Publication statusPublished - 10 Apr 2024
Externally publishedYes
Event6th International Conference on Mathematics and Mathematics Education, ICM2E 2022 - Padang, Indonesia
Duration: 3 Sept 20224 Sept 2022

Publication series

NameAIP Conference Proceedings
Number1
Volume3024
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference6th International Conference on Mathematics and Mathematics Education, ICM2E 2022
Country/TerritoryIndonesia
CityPadang
Period3/09/224/09/22

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