Discovering Meaningful Pattern of Undergraduate Students Data using Association Rules Mining
- 10.2991/adics-es-19.2019.4How to use a DOI?
- data mining, association rules mining, apriori algorithms, frequent itemsets mining, student undergraduate data, knowledge discovery, data patterns
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.
- © 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/).
Cite this article
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 -