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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) INOVTEK Polbeng - Seri Informatika
Salsabilla Azahra Putri
Universitas Ahmad Dahlan
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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Intelligent Monitoring of Smoking Prohibition in Public Spaces Using YOLOv8: Real-Time Detection and Telegram Notifications Salsabilla Azahra Putri; Murinto; Sunardi
Jurnal Penelitian Pendidikan IPA Vol 11 No 4 (2025): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i4.10519

Abstract

This study aims to develop an intelligent monitoring system that supports the enforcement of smoking prohibition in public spaces by leveraging advancements in Artificial Intelligence (AI) and deep learning. Utilizing the YOLOv8 (You Only Look Once version 8) object detection model, the system is designed to identify smoking activities in real-time and promptly send alerts through the Telegram messaging platform. The proposed method integrates real-time object detection with an automated notification system, ensuring responsive enforcement across diverse environmental conditions, including normal lighting, low-light scenarios, and partially occluded views. The system architecture combines the YOLOv8 model for detection and a Python-based Telegram bot for communication. The model was evaluated using a test dataset collected from various public spaces. It achieved an F1-Score of 81% and a confusion matrix accuracy of 89%, indicating a high level of reliability and precision in identifying smoking behaviors. Additionally, the average notification response time via Telegram was 1.5 seconds, enabling near-instantaneous alerting for enforcement personnel. In conclusion, the results demonstrate that the system is both accurate and efficient in detecting smoking activities. Its robust performance across different conditions and rapid alert mechanism positions it as a practical and scalable solution to enhance compliance with smoking regulations in public areas.
Detecting Smoking Activity Behavior using YOLOv8 and YOLOv11 Salsabilla Azahra Putri; Murinto; Sunardi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0q6grf14

Abstract

Smoking behavior in public spaces remains a major challenge in the implementation of public health policies, particularly within designated smoke-free zones. This study aims to examine whether architectural improvements and spatio-temporal modeling in object detection models can enhance the accuracy of real-time smoking behavior detection. Specifically, the performance of YOLOv8 and an experimental version, YOLOv11, is compared using a vision-based approach. A dataset of 3,000 annotated images is used, consisting of smoking and non-smoking activities such as drinking or phone use, with variations in lighting, body posture, and camera angles. The dataset was divided into 80% for training, 20% for validation, and 20% for testing, with data augmentation applied to improve generalization. YOLOv11 incorporates spatio-temporal modules and attention mechanisms not present in YOLOv8. Evaluation results show that YOLOv11 outperforms YOLOv8, achieving a Precision of 0.95, Recall of 0.91, and F1-Score of 0.93, while YOLOv8 reached 0.89, 0.87, and 0.88 respectively. These findings indicate that YOLOv11 offers a more robust and adaptive solution for automatically recognizing smoking behavior in real-world environments and supports the development of intelligent surveillance systems for enforcing smoke-free policies.
Co-Authors Murinto Murinto Sunardi, Sunardi