Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)

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Finding Recommended Feature on Student Enrolment Dataset of University XYZ Using Exploratory Data Analysis (EDA)

Authors
Zulkarnaini Zulkarnaini1, Indra Griha Tofik Isa1, *, Leni Novianti1, Febie Elfaladonna1, Suzan Agustri2
1Politeknik Negeri Sriwijaya, Palembang, South Sumatera, Indonesia
2Universitas Indo Global Mandiri, Palembang, Indonesia
*Corresponding author. Email: indra_isa_mi@polsri.com
Corresponding Author
Indra Griha Tofik Isa
Available Online 26 June 2023.
DOI
10.2991/978-94-6463-118-0_42How to use a DOI?
Keywords
Exploratory Data Analysis (EDA); Enrolment Student Dataset; Data Understanding
Abstract

One of the success of a modelling is the quality of the analysed data. Exploration Data Analysis is a technique used in understanding data to explore which data has quality which will be used in modelling. The case raised in this study is the student registration dataset at XYZ University, where the ultimate goal is how to predict study program preferences for prospective applicants. However, from this data set with various data, it needs to be studied further to produce quality data that is valid, credible, and supports the modelling of preferences for the choice of study program. An EDA will be implemented as a solution to data analysis by looking at the variety of data from the student enrolment dataset, features that support modelling, recommendations that need to be made for advanced stages in a data science cycle. The stages of the research were Problem Analysis, Data Acquisition, Exploration Data Analysis, Anomaly Interpretation, and Feature Recommendations. The final result is in the form of 14 recommended features from the Student Registration Dataset consisting of Sex, Date of Birth, Study Program, Civil Status, Province, City, Child Order, Number of Siblings, Income, Education stage, Lecturing Program, Jenis Sekolah, Department of School, National Test Score, Year of Graduation.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)
Series
Atlantis Highlights in Engineering
Publication Date
26 June 2023
ISBN
978-94-6463-118-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-118-0_42How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zulkarnaini Zulkarnaini
AU  - Indra Griha Tofik Isa
AU  - Leni Novianti
AU  - Febie Elfaladonna
AU  - Suzan Agustri
PY  - 2023
DA  - 2023/06/26
TI  - Finding Recommended Feature on Student Enrolment Dataset of University XYZ Using Exploratory Data Analysis (EDA)
BT  - Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)
PB  - Atlantis Press
SP  - 407
EP  - 419
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-118-0_42
DO  - 10.2991/978-94-6463-118-0_42
ID  - Zulkarnaini2023
ER  -