Abstract—Science and technology advancement drives
humans to adapt to the digital world. IT development is proven
to positively affect the education area through the concept of
electronic learning (e-learning). This is especially true during
the COVID-19 pandemic where traditional classrooms teaching
was transferred to e-learning. This technological development
demands individuals to adapt to the advancement. Despite its
benefits, technological advancement may affect the physical
condition of e-learning users. When the e-learning users fail to
adjust, they might have physical condition problems that cause
depression. Therefore, we propose an Internet of Things
(IoT)-based system to detect the physiological conditions of
e-learning users. By implementing Fuzzy Tsukamoto as
artificial intelligence on IoT technology, we can identify the
physiological condition of e-learning users such as relaxed, calm,
anxious, and stressed conditions. Structurally, the proposed
system consists of three stages: 1) Sensor data acquisition, 2)
Physiological condition detection using Fuzzy Tsukamoto, 3)
Display the output directly to the website. We evaluate the
effectiveness of the proposed system in the task of detecting the
physiological condition of the ten e-learning users. Based on
experimental results, the proposed system presents 84.01% of
accuracy. This result indicates that the proposed system is able
to reliably detect physiological conditions on IoT-based
e-learning users. By detecting psychological conditions,
e-learning is expected to become an adaptive learning system so
that it can adapt to the characteristics of each user.
Index Terms—Fuzzy Tsukamoto, physiological condition, Internet of Things, e-learning.
F. Pradana and F. A. Bachtiar are with the Department of Information System, Faculty of Computer Science, Universitas Brawijaya, Malang 65145, Indonesia (e-mail: email@example.com, firstname.lastname@example.org).
E. R. Widasari is with the Department of Computer Engineering, Faculty of Computer Science, Universitas Brawijaya, Malang 65145, Indonesia (e-mail: email@example.com).
Cite: F. Pradana, F. A. Bachtiar, and E. R. Widasari, "Fuzzy Tsukamoto Implementation to Detect Physiological Condition on IoT-Based e-Learning Users," International Journal of Information and Education Technology vol. 12, no. 7, pp. 663-667, 2022.Copyright © 2022 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).