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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Al Furqan: Jurnal Ilmu Al Quran dan Tafsir Buletin Ilmiah Sarjana Teknik Elektro Aktual : Jurnal Pengabdian Kepada Masyarakat Journal of Educational Technology and Learning Creativity International Journal of Advances in Artificial Intelligence and Machine Learning Khazanah Hukum
Deshinta Arrova Dewi
INTI International University
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection Anggy Pradifta Junfithranaa; Hadi Almohab; Deshinta Arrova Dewi
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.
Integrated Virtual Reality Learning Framework with Digital Ecosystem for Enhancing Physics Conceptual Understanding Eko Risdianto; Joselin Santos; Noel Lomerio; Deshinta Arrova Dewi; M. Esad Kuloglu; Sultan Hammad Alshammari; Ressti Nurfitriani
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.v4i1.2955

Abstract

Purpose of the study: This study develops and evaluates an integrated virtual reality learning framework to improve high school students’ physics conceptual understanding. The framework combines immersive 360° virtual reality videos via Kuula with Google Classroom, ClassPoint, digital flipbooks, and PhET simulations within a problem-based learning environment. Methodology: This study employed a research and development approach using the ADDIE model, combined with a pre-experimental one-group pretest–posttest design. The participants consisted of three cohorts of high school students (n = 115) across different physics topics: Kinematics (n = 36), fluids (n = 39), and particle dynamics (n = 40). The framework was validated by three experts using structured instruments assessing content, media, language, and presentation aspects. Data were collected through validation sheets, student response questionnaires, and conceptual understanding tests. Data analysis included percentage-based measures, N-Gain, Shapiro Wilk tests, paired t-tests, Wilcoxon signed rank tests, and effect size calculations. Main Findings: The results indicate high validity (88.89%–97%) and practicality (86%–98%). The implementation was associated with significant improvements in conceptual understanding, reflected in high N-Gain scores (0.81–0.87) and large effect sizes (p < 0.05). These findings suggest that the integrated virtual reality based learning ecosystem can effectively support conceptual understanding within the studied context. Novelty/Originality of this study: The novelty lies in the systematic integration of the Kuula platform within a multi-component digital learning ecosystem under a problem based learning framework, as well as its application across multiple physics topics to demonstrate consistent learning outcomes.
Robotics Workshop to Increase Motivation and Science Literacy of SMP IT Khairu Ummah Rejang Lebong Students Afrizal Mayub; M. Lutfi Firdaus; Fahmizal Fahmizal; Aceng Ruyani; Lazfihma Lazfihma; Deshinta Arrova Dewi
Aktual: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2025): Aktual: Jurnal Pengabdian Kepada Masyarakat January 2025
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/aktual.v3i1.347

Abstract

This activity aims to Increase the Motivation and Science Literacy of SMP IT Khairu Ummah Rejang Lebong Students. This activity is one form of implementing the tridharma of higher education. One of the tri dharmas of higher education is community service, this service activity is an activity that every lecturer must carry out, therefore lecturers in the Master of Science program at Bengkulu University collaborated with IT SMP teacher Khairu Ummah Rejang Lebong Bengkulu on September 28, 2024, to carry out service activities at SMP IT Khairu Ummah Rejang Lebong. This collaboration takes the form of community service activities at SMP IT Khairu Ummah Rejang Lebong Bengkulu with the title "Robotics Workshop to Increase Motivation and Science Literacy of SMP IT Khairu Ummah Rejang Lebong Students". From these activities, it can be concluded that the teaching material delivered at the Robotics Workshop uses interactive multimedia which includes lectures, virtual demos, animations, visualizations, simulations and videos which have proven successful in motivating students in the motivated category with a score of 3.84 and increasing scientific literacy in the high category with a score of 3.93.
Co-Authors Abd Muin Abhijit Banubakode ACENG RUYANI Afrizal Mayub Ahmad Bahrudin Alfarid Hendro Yuwono Aliyu Sabo Anggy Pradifta Junfithranaa Cecep Soleh Kurniawan Dani Durahman Dhendra Maruhto Eko Risdianto Eva Yulia Puspaningrum Fahmizal Fahmizal Giraldo Stevanus Hadi Almohab Hajjar Ayu Cahyani Kuswardhani Harsha J. Sarode I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Joselin Santos JosephNg Poh Soon Lazfihma Lazfihma Liton Chandra Paul M. Esad Kuloğlu M. Lutfi Firdaus M. Tohir Mahesh Shankarrao Patil Malathy Batumalay Margareta Valencia Suci Handayani Md. Abdul Kader Jilani Md. Ashraful Haque Md. Sharif Ahammed Mohammad Idhom Muljono, - Nafi Isbadrianingtyas Narinderjit Singh Sawaran Singh Noel Lomerio Nutan Patil Pangestu Sandya Etniko Siagian Panji Adam Agus Putra Prakash Tukaram Patil Pramudita, Yoga Dwitya Prismahardi Aji Riyantoko Raden Arief Nugroho Rajani Balakrishnan Rajermani Thinakaran Redwan A. Ananta Ressti Nurfitriani Reza Rahmadian Ruri Suko Basuki Sayyidah Humairah Suhardini, Eni Dasuki Sultan Hammad Alshammari Ugochi Okengwu Vijayakumar Varadarajan Vugar Hacimahmud Abdullayev Wahyudi Agustiono Wahyudi Setiawan Wan Suryani Wan Awang Widi Aribowo Yo Ceng Giap Zakka Ugih Rizqi