CodelessML: A Beginner's Web Application for Getting Started with Machine Learning

Hanif Noer Rofiq; Galuh Mafela Mutiara Sujak

doi:10.18009/jcer.1506864

TR EN

CodelessML: A Beginner's Web Application for Getting Started with Machine Learning

Abstract

Building machine learning models requires intensive coding and installation of certain software. This is frequently a barrier for beginners learning about machine learning. To overcome this situation, we present CodelessML, a reproducible web-based application designed for Machine Learning beginners due to its coding-free and installation-free design, published under Code Ocean capsule. It provides a common workflow that eases the process of building Machine Learning models and using the model for predictions. Using the Agile method, CodelessML was successfully built using Python, Anaconda, and Streamlit It. By using CodelessML, users can get a walkthrough and interactive experience of building machine learning through a simplified machine learning process: exploratory data analytics (EDA), modelling, and prediction. The impact of the software was evaluated based on feedback from 79 respondents, which showed that based on a 5-scale Likert, CodelessML received average ratings of 4.4 in accessibility, 4.3 in content, and 4.4 in functionality. CodelessML serves as an accessible entry point for learning machine learning, offering online, free, and reproducible features.

Keywords

Machine learning, learning, barrier, software

Project Number

01

Ethical Statement

Acknowledgement Due to the scope and method of the study, ethics committee permission was not required.

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Details

Primary Language

English

Subjects

Development of Science, Technology and Engineering Education and Programs, Educational Technology and Computing

Journal Section

Research Article

Authors

Hanif Noer Rofiq ^*
0009-0004-0234-3055
Indonesia

Galuh Mafela Mutiara Sujak This is me
0009-0004-6706-5308
Indonesia

Early Pub Date

September 17, 2024

Publication Date

October 21, 2024

Submission Date

June 28, 2024

Acceptance Date

September 6, 2024

Published in Issue

Year 2024 Volume: 12 Number: 24

DOI

https://doi.org/10.18009/jcer.1506864

IZ

https://izlik.org/JA97BA72RA

APA

Rofiq, H. N., & Sujak, G. M. M. (2024). CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning. Journal of Computer and Education Research, 12(24), 582-599. https://doi.org/10.18009/jcer.1506864

AMA

1.Rofiq HN, Sujak GMM. CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning. JCER. 2024;12(24):582-599. doi:10.18009/jcer.1506864

Chicago

Rofiq, Hanif Noer, and Galuh Mafela Mutiara Sujak. 2024. “CodelessML: A Beginner’s Web Application for Getting Started With Machine Learning”. Journal of Computer and Education Research 12 (24): 582-99. https://doi.org/10.18009/jcer.1506864.

EndNote

Rofiq HN, Sujak GMM (October 1, 2024) CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning. Journal of Computer and Education Research 12 24 582–599.

IEEE

[1]H. N. Rofiq and G. M. M. Sujak, “CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning”, JCER, vol. 12, no. 24, pp. 582–599, Oct. 2024, doi: 10.18009/jcer.1506864.

ISNAD

Rofiq, Hanif Noer - Sujak, Galuh Mafela Mutiara. “CodelessML: A Beginner’s Web Application for Getting Started With Machine Learning”. Journal of Computer and Education Research 12/24 (October 1, 2024): 582-599. https://doi.org/10.18009/jcer.1506864.

JAMA

1.Rofiq HN, Sujak GMM. CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning. JCER. 2024;12:582–599.

MLA

Rofiq, Hanif Noer, and Galuh Mafela Mutiara Sujak. “CodelessML: A Beginner’s Web Application for Getting Started With Machine Learning”. Journal of Computer and Education Research, vol. 12, no. 24, Oct. 2024, pp. 582-99, doi:10.18009/jcer.1506864.

Vancouver

1.Hanif Noer Rofiq, Galuh Mafela Mutiara Sujak. CodelessML: A Beginner’s Web Application for Getting Started with Machine Learning. JCER. 2024 Oct. 1;12(24):582-99. doi:10.18009/jcer.1506864