Green Intelligent Systems and Applications

Integrating TOGAF 10 and ISO 20000-1:2018 for Digital Multi-finance Service Level Agreement/Mean Time to Repair improvements

2026-01-30T06:52:40+00:00

Digital transformation in the multi-finance sector demands service architectures that are flexible, reliable, and scalable; however, misalignment between architectural design and operational execution often leads to weak service performance. This study proposes an integrated framework that combines TOGAF 10 artifacts with ISO/IEC 20000-1:2018 processes to systematically estimate Service Level Agreement (SLA) targets and reduce Mean Time to Repair (MTTR). Using a Design Science Research approach, the framework was implemented in a 14-month case study at PT XYZ Multi-finance. The resulting artifacts include a bidirectional traceability model linking business objectives to SLA and MTTR indicators, as well as an operability pattern catalog to support “design for operability.” The implementation delivered measurable operational improvements: MTTR decreased from a peak of 775 minutes to below 60 minutes, Mean Time to Detect (MTTD) was reduced by approximately 90%, SLA compliance increased to 99.7%, and incidents caused by manual configuration errors declined. These results demonstrate that integrating enterprise architecture design with service management processes can significantly improve service reliability and overall operational performance.

An Image Processing-Based Fire Detection System Using Orange Pi 4A with Internet of Things Integration in Indoor Environments

2026-02-14T01:44:22+00:00

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

2026-02-14T01:44:53+00:00

: 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.