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IJIET 2024 Vol.14(1): 72-80
doi: 10.18178/ijiet.2024.14.1.2026

Embedded System Training Kit for Artificial Intelligence

Wahyu Ramadhani Gusti* and Fatchul Arifin
Department of Electronics and Informatics Engineering Education, Yogyakarta State University, Indonesia
Email: wahyuramadhani.2021@student.uny.ac.id (W.R.G.); fatchul@uny.ac.id (F.A.)
*Corresponding author

Manuscript received May 5, 2023; revised June 29, 2023; accepted August 16, 2023; published January 18, 2024

Abstract—Ever-developing science and technology require us always to be ready to adapt. The current challenging era is Society 5.0, which places a strong emphasis on harnessing human potential to overcome diverse challenges, including the development of Artificial Intelligence (AI) technology. Therefore, to improve the quality of human resources, this paper proposes the development of an artificial intelligence training kit based on embedded systems according to industry needs. The development of a training kit utilizing the RnD method was accomplished through the use of the ADDIE (analysis design, development, implementation, and evaluation) model. This model encompasses analysis, design, development, implementation, and evaluation. The technology of the training kit combines fuzzy logic, Artificial Neural Network (ANN), and image processing, consisting of hardware, software, and job sheets. The controller used to process embedded systems is the ESP32 board. Arduino UNO is used to execute the training results of the artificial intelligence system. The training kit performance test results show that all AI programs run optimally, and each component can function according to performance indicators. A group of subject matter and media experts evaluated the feasibility of the project and determined it to be very feasible, with a score of 83.64% and 86.67%. In addition, a feasibility test was conducted with 38 respondents, resulting in a score of 83.35%, and it was categorized as a very feasible tool. The effectiveness of the training kit applied to the experimental class resulted in a post-test mean score of 89.58, while the control class mean score was 76.39, so the AI training kit showed more effectiveness.

Keywords—Artificial Intelligence (AI) training kit, ADDIE model, embedded system, fuzzy logic, Artificial Neural Network (ANN), image processing

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Cite: Wahyu Ramadhani Gusti and Fatchul Arifin, "Embedded System Training Kit for Artificial Intelligence," International Journal of Information and Education Technology vol. 14, no. 1, pp. 72-80, 2024.


Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Managing Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net

 

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