cover
Contact Name
Rahmat Perdana
Contact Email
rahmat260997@gmail.com
Phone
-
Journal Mail Official
cic.jetlc@gmail.com
Editorial Address
Cahaya Ilmu Cendekia Publisher, Jl. Perumnas Griya Sungai Duren, No. 54 A, Jambi, Indonesia 36361
Location
Unknown,
Unknown
INDONESIA
Journal of Educational Technology and Learning Creativity
ISSN : 30253888     EISSN : 30217865     DOI : https://doi.org/10.37251/jetlc
Core Subject : Science, Education,
Covers all the Journal of Educational Technology and Learning Creativity at the level of primary, secondary, senior, and higher education. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Educational advancements and establishing new collaborations in these areas. Original research papers and state-of-the-art reviews are invited for publication in all areas of the Journal of Educational Technology and Learning Creativity. Topics of Interest include, but are not limited to the following: Innovation and best practices in online learning, training, research, and management Educational Technology, models, and Trends in Higher Education Computer-supported collaborative learning, training, and research Intelligent guidance and mentoring system Learning analytics and educational data mining Open access system for learning and training Organizational and administrative perspectives on the Use of IT in higher education University Governance and Leadership in the knowledge society Institutional policies, standards, and assessment methods Higher education Attendance and service models using the Internet Internationalization and cultural aspects of online learning, training, and research
Arjuna Subject : Umum - Umum
Articles 91 Documents
Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection Junfithranaa, Anggy Pradifta; Almohab, Hadi; Dewi, Deshinta Arrova
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2614

Abstract

Purpose of the study: This study aims to develop an intelligent indoor fire detection system by integrating low-cost Internet of Things (IoT) sensors with machine learning-based multi-sensor data fusion to improve early fire hazard detection accuracy while reducing false alarms compared to conventional single-sensor fire detection systems. Methodology: The system is implemented using an ESP32 microcontroller connected to temperature, humidity, flame, and sound sensors for real-time data acquisition. A dataset of 1,500 sensor samples is collected and labeled into Normal, Fire-Risk, and Fire classes. Decision Tree, Support Vector Machine, and Random Forest classifiers are trained and evaluated using Python-based machine learning libraries. Main Findings: Experimental results indicate that the Random Forest model outperforms the other classifiers, achieving 95% overall accuracy, perfect recall for fire events, and a Macro ROC-AUC score of 0.993. Feature importance analysis reveals that humidity and temperature are the most influential parameters for early fire detection in indoor environments. Novelty/Originality of this study: This study proposes a lightweight intelligent fire detection framework that integrates multi-sensor Internet of Things data including temperature, humidity, flame, and sound signals with machine learning–based classification for indoor environments. Unlike conventional systems that rely on single-sensor or threshold-based detection, the proposed approach utilizes multi-sensor data fusion and ensemble learning to improve early fire-risk identification while remaining computationally efficient for low-cost platforms such as the ESP32 microcontroller.

Page 10 of 10 | Total Record : 91