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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Bulletin of Information Technology (BIT) Brilliance: Research of Artificial Intelligence Green Intelligent Systems and Applications Informatics and Enginering Dedication SENTRI: Jurnal Riset Ilmiah MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems Jurnal Ilmiah Multidisiplin Indonesia Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer IPSSJ
Eka Puji Widiyanto
Universitas Multi Data Palembang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Journal : green intelligent systems and applications

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An Image Processing-Based Fire Detection System Using Orange Pi 4A with Internet of Things Integration in Indoor Environments Safeti Intan Pratiwi; Eka Puji Widiyanto
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1009

Abstract

Fire hazards in indoor industrial environments require fast and reliable detection systems, as conventional sensor-based methods often suffer from delayed responses and high false-alarm rates. This study proposes a low-cost, Internet of Things-integrated visual fire detection system based on the YOLOv11 deep learning model implemented on an Orange Pi 4A. The system integrates an IP camera for visual acquisition, real-time detection, and automatic data logging through a MySQL-based monitoring platform. Experiments were conducted in a 3 × 3 m indoor environment using candle, stove, and burning fires at various camera distances. System performance was evaluated using confidence score, bounding box pixel area, and recall based on True Positive and False Negative classifications. Candle flames were reliably detected up to 100 cm with recall values of 90.24%–100% and pixel areas below 5,000 px, while stove flames achieved recall above 93% at 50–100 cm with pixel areas of 11,144–42,525 px. Burning fires maintained high performance up to 300 cm, reaching confidence values above 0.70 and recall rates of 78.94%–100% with pixel areas exceeding 44,000 px. The results indicate that detection reliability is primarily influenced by apparent flame size rather than camera distance. Overall, the proposed system demonstrates strong feasibility as an embedded, IoT-integrated fire detection solution for early warning in indoor industrial environments, although limitations remain in detecting small flames under low-resolution and low-light conditions.
Design and Implementation of a Multi-Node Gas Sensor-Based Indoor Air Quality Monitoring and Control System Siti Milda Alkan Dawasoka; Eka Puji Widiyanto
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1012

Abstract

:  Air quality monitoring was a crucial aspect of maintaining occupational health and safety, particularly in industrial environments. This study proposed the design and implementation of an Internet of Things (IoT)-based indoor air quality monitoring system capable of measuring environmental parameters in real time. The system integrated an ENS160 gas sensor and an AHT21 temperature–humidity sensor with a Wemos D1 Mini microcontroller. Sensor data were transmitted via the MQTT protocol to an Orange Pi 4A server and visualized using a Node-RED dashboard. The monitored parameters included Total Volatile Organic Compounds (TVOC), equivalent CO₂ (eCO₂), temperature, and humidity. Experimental evaluation demonstrated that the system responded proportionally to different pollutant exposure levels. Under high NH₃ exposure (100%), TVOC values reached a maximum of 12,697 ppb with an average of 5,037 ppb, clearly exceeding the hazardous threshold (>200 ppb). At moderate exposure (50%), the average TVOC decreased to 2,106 ppb, while at low exposure (10%), the average value remained within the safe range at 84 ppb. For eCO₂ testing, cigarette smoke exposure produced a peak value of 11,524 ppm with an average of 1,663 ppm, indicating hazardous conditions (>1000 ppm). Statistical analysis using mean and standard deviation confirmed that sensor stability improved at lower pollutant concentrations. The proposed system successfully provided stable real-time monitoring, threshold-based classification, and automatic mitigation control, demonstrating its feasibility for intelligent indoor air quality management in industrial workspaces.
Co-Authors Abdul Rahman Adrian Chen Adrian Suparto alek bayu kurniawan Angel Kelly Brilliant Chandra Pratama Bryant Valencio Marpaung Dedy Hermanto Fahrezi, Muhammad Azril Feriyanto Feriyanto Fernandi Indi Nizar G Fernando Feliansyah Hafiz Irsyad Hartati, Ery Hendra Kurniawan Jovansa Putra Laksana Khrisnaldi Wijaya Michael Joy Clement Muhammad Reihan Daffa Putra Muhammad Rizky Pribadi Muhammad Tri Setianto Raymondus Trisaptono Ricky Ricky Rizky Ade Kusuma Sa'adat, Fadhil Safeti Intan Pratiwi Safeti Intan Pratiwi Siti Milda Alkan Dawasoka Stephen Setyawan Therino Elevan, Rivaldo William Wijaya Willyam Lucas Haposan Sumual