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Heri Retnawati
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Journal of Robotics, Automation, and Electronics Engineering
Published by Universitas Negeri Yogyakarta
ISSN : 30254590     EISSN : 30253780     DOI : 10.21831
Core Subject : Engineering,
Electrical & Electronics Engineering Engineering
Focus and Scope Subject areas suitable for publication in the Journal of Robotics, Automation, and Electronics Engineering (JRAEE) covering various fields related to, 1. Robotics: - Robot design, kinematics, and dynamics - Motion planning and control of robots - Soft robotics and flexible mechanisms - Human-robot interaction and collaboration - Robot perception and computer vision - Swarm robotics and multi-robot systems - Field robotics and autonomous vehicles 2. Automation: - Industrial automation and smart manufacturing - Process control and optimization - Intelligent control systems and adaptive control - Home and building automation systems - Automation in agriculture and healthcare - Robotic automation in logistics and warehouses - Cyber-physical systems and IoT in automation 3. Electronics Engineering: - Analog and digital circuit design - Microelectronics and VLSI design - Power electronics and renewable energy systems - Sensors and actuators technology - Signal processing and communication systems - Electronic instrumentation and measurement - Wearable electronics and health monitoring devices 4. Artificial Intelligence (AI) in Robotics and Automation: - Machine learning algorithms for robotics - Reinforcement learning for autonomous systems - AI-based decision-making and planning in robotics - Computer vision and image processing in automation - Natural language processing for human-robot interaction - AI-enabled control and optimization in automation - Explainable AI in robotics and automation 5. Mechatronics: - Integration of mechanical, electronic, and control systems - Design and analysis of mechatronic systems - Mechatronic applications in industry and research - Micro- and nano-mechatronics - Intelligent sensors and actuators in mechatronics - Mechatronic control algorithms and strategies - Rapid prototyping and 3D printing in mechatronics 6. Internet of Things (IoT) Technology and Applications: - IoT architecture, protocols, and standards - IoT platforms and frameworks - Edge computing and fog computing for IoT - IoT security, privacy, and trust mechanisms - IoT in smart cities and urban infrastructure - IoT applications in healthcare and telemedicine - IoT for environmental monitoring and sustainability 7. Ethics and Social Implications of Robotics and Automation: - Ethical considerations in autonomous systems - Human-robot interaction ethics - Impact of automation on society and employment - Legal and regulatory aspects of robotics and AI - Privacy and security in robotics and automation - Social acceptance and public perception of robots - Bias and fairness in AI and robotics systems Authors conducting research and investigations within these areas are encouraged to submit their scholarly contributions to JRAEE for potential publication and dissemination of knowledge in these rapidly evolving fields.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 35 Documents
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Design And Development Of A Microcontroller-Based Air Pollution Monitoring System At Traffic Light Areas Using The Mamdani Fuzzy Logic Method Pangeran Antonius; Arya Sony
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.2038

Abstract

Air pollution was a serious issue in Indonesia, particularly in areas with high vehicle density, such as Yogyakarta. In 2024, more than 164 million vehicles were recorded in Indonesia. Motor vehicle emissions contributed to approximately 85% of total air pollution in the country. The high activity of motor vehicles increased the concentration of harmful gases such as carbon monoxide (CO), which had adverse effects on public health. To assess air pollution levels in surrounding areas, a monitoring system of air quality was required to maintain acceptable environmental conditions. In this study, a system was designed by integrating the MQ-135 sensor to detect carbon monoxide (CO) in real-time. The data obtained from the sensor were transmitted to the ESP32, where data processing employed the Mamdani Fuzzy Logic method to classify air quality into three categories: Good, Moderate, and Poor. The process began with CO level detection and was followed by fuzzification, inference, and defuzzification stages, producing a crisp value as a reference for air quality classification. In addition, the system was equipped with a monitoring feature based on Firebase and an application developed with MIT App Inventor. Based on the results of testing, the air pollution monitoring system was successfully implemented. The MQ-135 sensor was able to read surrounding air conditions in real-time with an average error of 0.33%, resulting in a reading accuracy level of 99.67%. The Mamdani Fuzzy Logic method effectively processed ambiguous data; however, testing also identified limitations in the design of membership functions, which led to mismatches in category determination at threshold values. The system successfully transmitted data in real-time to Firebase and the MIT App Inventor application with a success rate of 80%.
A Real-Time Road Damage Monitoring System using YOLOv11 on an Edge Computer Zaki Nugraha; Aris Nasuha
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.2190

Abstract

This study developed a real-time road damage detection system on an edge device to address the inefficiency and subjectivity of manual monitoring. Following the Engineering Design Process (EDP), the system uses a YOLOv11 model—trained in PyTorch and converted to ONNX for optimization—to classify four types of road damage on a Raspberry Pi 5, integrated with a GPS module for automatic coordinate recording. Field evaluation showed that after optimization, the system achieved an average processing speed of 3.66 FPS with an inference latency of 277.69 ms, while maintaining efficient resource usage (45.06% RAM, 65.05% CPU load) and an mAP@50 of 0.554. These results demonstrate the feasibility of an autonomous, accurate, real-time road damage detection system on a low-cost edge computing platform, producing geospatial data ready to support operational decision-making in road maintenance.
Prototype of a Coffee Bean Weight Measuring Device Using a Webcam with the Convolutional Neural Network (CNN) Method at Roetin Coffee Shop Nurul Budi; Faris Yusuf Baktiar
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.2375

Abstract

The advancement of Artificial Intelligence (AI) and Computer Vision has enabled new opportunities for automation within the coffee industry, particularly in weight measurement of coffee beans, which is still performed manually and becomes inefficient at large scale. This study proposes an automatic weight estimation system using images captured by a webcam and processed through a Convolutional Neural Network (CNN) employing MobileNet as a lightweight regression model. The developed system analyzes visual features to estimate weight autonomously, offering an efficient, contactless alternative to conventional weighing tools and supporting stock monitoring for coffee industries and small enterprises.
Air Quality Classification Using the K-Nearest Neighbors Algorithm: A Case Study in Juwana District Tanzila Bagus Ramadhan; Dessy Irmawati
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.2553

Abstract

Air quality is an important factor in maintaining human health and environmental sustainability. Increasing transportation activity in Pati Regency contributes to higher air-pollutant emissions. This study develops a real-time air-quality monitoring and classification system using the K-Nearest Neighbors (KNN) algorithm. The system uses MQ-7, MQ-135, and SDS011 sensors to measure CO, CO₂, PM2.5, and PM10 concentrations. Measurement data are transmitted to Firebase Realtime Database and classified through a graphical user interface using Euclidean distance. Experimental results show that the system achieves 83% accuracy, 85% precision, 83% recall, and an F1-score of 82%. These results indicate that the proposed system can provide reliable real-time air-quality information for environmental monitoring applications.
Integrating Color Segmentation and Texture Analysis for Citrus Leaf Disease Identification through K-Means and Local Binary Pattern Armin; Suharsono Bantun; Jayanti Yusmah Sari
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.3827

Abstract

Early identification of citrus leaf diseases is important for reducing crop losses and supporting effective disease management. This study proposes a citrus leaf disease identification framework combining K-means color segmentation and Local Binary Pattern (LBP) texture analysis. The method was evaluated using 90 citrus leaf images from three disease classes: Citrus Canker, Leaf Miner, and Sooty Mold. Images were preprocessed through cropping, resizing, and color space transformation. K-means clustering in the Lab color space was used to segment diseased regions, while LBP extracted texture features. Classification was performed using Euclidean Distance and evaluated with 10-fold cross-validation. The K-means-based method achieved an average accuracy of 76.65%, while LBP alone obtained 45.65%. Combining K-means and LBP produced the best performance, with an average accuracy of 80.02%. These results indicate that integrating color and texture features provides a more effective representation of citrus leaf disease symptoms and improves classification performance.

Page 4 of 4 | Total Record : 35

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