Discovering Meaningful Pattern of Undergraduate Students Data using Association Rules Mining

DOI

10.2991/adics-es-19.2019.4How to use a DOI?

Keywords

data mining, association rules mining, apriori algorithms, frequent itemsets mining, student undergraduate data, knowledge discovery, data patterns

Abstract

Association rules mining is a technique in data mining to discovering a meaningful pattern of data. The main objective of this research is to identify undergraduate students data and to get the profile and insight from the past data. It will have a benefit for improvement in academic activity in the future. This research has two phases. The first phase is preprocessing data, and the second phase is analyzing and measurement data using the Apriori Algorithms. The data preprocessing stage is done by cleaning data from noise and transforming data into the specified parameters. We use four feature/variable data, namely length of study duration, length of thesis duration, and Grade Point Average (GPA), and English proficiency score. The results of this research are variables of English proficiency score, Grade Point Average (GPA), and length of study duration having relations in student data.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Herman Yuliansyah
AU  - Hafsah
AU  - Ika Arfiani
AU  - Rusydi Umar
PY  - 2019/11
DA  - 2019/11
TI  - Discovering Meaningful Pattern of Undergraduate Students Data using Association Rules Mining
BT  - Proceedings of the 2019 Ahmad Dahlan International Conference Series on Engineering and Science (ADICS-ES 2019)
PB  - Atlantis Press
SP  - 43
EP  - 47
SN  - 2352-5401
UR  - https://doi.org/10.2991/adics-es-19.2019.4
DO  - 10.2991/adics-es-19.2019.4
ID  - Yuliansyah2019/11
ER  -