Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

Analysis Comparison of Data Mining Algorithm for Prediction Student Graduation Target

Authors
Rachmadita Andreswari, Muhammad Azani Hasibuan, Dela Youlina Putri, Qalbinuril Setyani
Corresponding Author
Rachmadita Andreswari
Available Online March 2019.
DOI
https://doi.org/10.2991/icoiese-18.2019.58How to use a DOI?
Keywords
data mining, education, c4.5, fuzzy AHP, decision support system.
Abstract
The main objective of a higher education institution is to provide quality education for its students. The most important indicator to measure the quality of higher education performance is the percentage of student graduation on time. However, not all student can successfully have completed their studies during the four years of normal study period where it became problems for academic planners. So, it can affect to the study program accreditation assessment. In this study, C4.5 algorithms and fuzzy AHP are used to predict the number of students graduating on time. An analysis has conducted on how students can graduate on time and plan strategies for groups of students who are likely not to graduate on time. Furthermore, a comparative analysis of the algorithms that have been implemented which will provide more precise and accurate results. Data processing is carried out using the Rapidminer application. The results of the student graduation target analysis were found that the main factor of graduation on time using FAHP was the number of repeating courses (do not pass courses), while in C4.5 it was caused by GPA level 2. Both algorithms had a good level of accuracy, where FAHP and C4.5 were 100% and 82.24% respectively. This research can be used as a reference basis for supporting academic planners in making the right decisions for student groups produced so that all students can graduate on time.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
Part of series
Atlantis Highlights in Engineering
Publication Date
March 2019
ISBN
978-94-6252-689-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icoiese-18.2019.58How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Rachmadita Andreswari
AU  - Muhammad Azani Hasibuan
AU  - Dela Youlina Putri
AU  - Qalbinuril Setyani
PY  - 2019/03
DA  - 2019/03
TI  - Analysis Comparison of Data Mining Algorithm for Prediction Student Graduation Target
BT  - 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
PB  - Atlantis Press
SP  - 328
EP  - 333
SN  - 2589-4943
UR  - https://doi.org/10.2991/icoiese-18.2019.58
DO  - https://doi.org/10.2991/icoiese-18.2019.58
ID  - Andreswari2019/03
ER  -