Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)

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Extracting the Recommended Features from the Elementary School Student Dataset through Exploration Data Analysis (EDA)

Authors
Devi Sartika1, Febie Elfaladonna1, *, Indra Griha Tofik Isa1, Andre Mariza Putra1
1Politeknik Negeri Sriwijaya, Palembang, South Sumatera, Indonesia
*Corresponding author. Email: febie_elfaladonna_mi@polsri.com
Corresponding Author
Febie Elfaladonna
Available Online 27 February 2024.
DOI
10.2991/978-94-6463-386-3_37How to use a DOI?
Keywords
Exploratory Data Analysis (EDA); Elementary School Student Dataset; Chi Square; T-test
Abstract

Exploratory data analysis (EDA) is an important stage in a data science cycle. In this research, the EDA process is carried out on the elementary school student dataset derived from the student “interest” and “talent” questionnaires. The purpose of this research is to find recommended features that will be used in the modeling stage. The main methods used in the implementation of EDA are chi square and T-test on the dependent variable, “class” and fifteen dependent variables. The stages were carried out by (1) analyzing the documents, data, and participants; (2) developing the questionnaire; (3) implementing the Likert and Yes/No questions; (4) formatting the data into tabular data; (5) coding and exploratory data analysis; (6) interpreting the findings and conclusion. From the results of chi square testing, the highest value was obtained in the “excellent in acting” variable with a value of 17.79284731, while the lowest result was found in the “writing, reading or storytelling” variable with a value of 0.29389977. Through the T-test, 3 categories of variable influence were obtained, i.e., “strong”, “moderate”, and “weak”.

Copyright
© 2024 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 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
Series
Advances in Engineering Research
Publication Date
27 February 2024
ISBN
10.2991/978-94-6463-386-3_37
ISSN
2352-5401
DOI
10.2991/978-94-6463-386-3_37How to use a DOI?
Copyright
© 2024 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  - Devi Sartika
AU  - Febie Elfaladonna
AU  - Indra Griha Tofik Isa
AU  - Andre Mariza Putra
PY  - 2024
DA  - 2024/02/27
TI  - Extracting the Recommended Features from the Elementary School Student Dataset through Exploration Data Analysis (EDA)
BT  - Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
PB  - Atlantis Press
SP  - 338
EP  - 351
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-386-3_37
DO  - 10.2991/978-94-6463-386-3_37
ID  - Sartika2024
ER  -