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All Journal JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Transmisi: Jurnal Ilmiah Teknik Elektro TRANSIENT: Jurnal Ilmiah Teknik Elektro Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) ELINVO (Electronics, Informatics, and Vocational Education) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JE-Unisla Jurnal Pengabdian Masyarakat Indonesia Prima Abdika: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bangsa Journal of Robotics, Automation, and Electronics Engineering
Arya Sony
Universitas Negeri Yogyakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : journal of robotics automation and electronics engineering

 1 
Design and Implementation of a Student Counting and Monitoring System in a Laboratory Using Human Tracking Method with OpenCV and TensorFlow Nancy Febriani Taek; Arya Sony
Journal of Robotics, Automation, and Electronics Engineering Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v2i1.554

Abstract

Laboratories serve as crucial facilities supporting practical activities, with a recommended maximum of 20 students, necessitating periodic monitoring to count the dynamic number of students within. The system utilizes the COCO dataset labeled ”person,” involving an approach with entry and exit preference lines, ID identification implementation, and object detection models YOLO v3 Tiny and Faster R-CNN ResNet50. The main system components, Raspberry Pi 3 Model B+, Raspberry Pi Camera 5 MP (f/1.3), and Raspberry Pi 7-inch Touch Display, are integrated for processing, real-time video recording, and image display functions. Test and evaluation results reveal that YOLO v3 Tiny achieves an 88.24% accuracy for entry counting and 75% for entry-exit counting, with an average processing rate of 4.89 FPS, while Faster R-CNN ResNet50 demonstrates lower accuracy, reaching 70.59% and 45.83%, with an average processing rate of 0.58 FPS.
Design of an Automatic Number Plate Recognition System Using PaddleOCR Method and Monitoring Application for Automated Parking Derizqiadi Thoriq; Arya Sony
Journal of Robotics, Automation, and Electronics Engineering Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v3i2.1978

Abstract

Manual parking systems suffer from weaknesses such as inefficiency, the risk of human error, and the prevalence of illegal parking and unauthorized fees. Therefore, an automated parking system is needed to improve efficiency, reduce human error, and facilitate integrated monitoring and management of parking. This system is designed to perform automatic number plate recognition (ANPR) and monitor parking activities, thereby reducing human error and enhancing user convenience. The system is designed using the PaddleOCR method, a deep learning-based OCR framework capable of reading text on vehicle license plates with high accuracy. The license plate recognition process begins with the detection of the license plate location, followed by reading the plate using the PaddleOCR method combined with image pre-processing and post-processing techniques. The resulting data from the license plate reading is transmitted to the Firebase and SQLite databases and monitored through the MIT App Inventor application. By integrating Raspberry Pi 5, a webcam, an OLED display, and an LED, this system is expected to optimize automated parking systems. Test results show that the developed system has excellent performance. The PaddleOCR model, combined with pre-processing and post-processing techniques, achieved a license plate character reading accuracy of 87.5%. This success was supported by the YOLOv10n detection model, which detected license plates with 90% accuracy under real-world testing conditions. All hardware components, such as the OLED display, LED, and buzzer, functioned with 100% reliability in providing notifications, and the system successfully transmitted data consistently to the Firebase, SQLite databases, and the monitoring application without any issues.
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%.
Co-Authors Agung Utama Ahmad Nugroho Jati Anis Okta Cahyaningrum Anita Mustikasari Ardy Priambodo Derizqiadi Thoriq Donald Jupply Gilang Pratama Hartoyo Hendarto, Taufik Maman Somantri Maria Lies Endarwati Masduki Zakarijah Mashoedah Mashoedah Nancy Febriani Taek Oktaf Dhewa Pangeran Antonius Pramudi Utomo Purno Tri Aji Rizky Hidayat Prasetyo Sa'adillah Rosyadi Selo Sulistyo, Selo Septian Rahman Hakim Suprapto Suprapto Taufik Hendarto Umi Rochayati Usman Nursusanto