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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Pengabdian Masyarakat Bumi Raflesia Cyber Security dan Forensik Digital (CSFD) Masyarakat Berdaya dan Inovasi Humanism : Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika
Murinto
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Journal : Jurnal Penelitian Pendidikan IPA (JPPIPA)

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Advanced Chicken Breed Identification Using Transfer Learning Techniques with the VGG16 Convolutional Neural Network Architecture kencana, Nagala Wangsa; Umar, Rusydi; Murinto
Jurnal Penelitian Pendidikan IPA Vol 11 No 7 (2025): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i7.11870

Abstract

This study proposes a deep learning-based classification system to identify chicken breeds from image data. A dataset of 2,400 labeled images representing twelve distinct chicken breeds was collected and divided into training, validation, and testing sets. The system employs transfer learning by integrating the Mobile VGG16 convolutional neural network as the feature extraction backbone. The extracted features were then passed through custom classification layers to differentiate among the breeds. The model was trained using 1,800 images, validated with 300 images, and evaluated on a separate test set of 300 images. During testing, the model achieved an accuracy of 81% and a categorical cross-entropy loss of 0.378. These results indicate that the model can effectively recognize subtle visual distinctions between similar-looking chicken breeds. The system demonstrates practical potential for applications in poultry farming, biodiversity documentation, and automated livestock management. The findings confirm that deep convolutional neural networks, particularly VGG16 in a transfer learning setup, are capable of performing fine-grained classification tasks in real-world scenarios. The proposed method provides a reliable and scalable solution for automatic chicken breed identification based on image input.
Intelligent Monitoring of Smoking Prohibition in Public Spaces Using YOLOv8: Real-Time Detection and Telegram Notifications Putri, Salsabilla Azahra; 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.
Co-Authors Abdul Fadlil Abdul Fadlil Agung Tri Lestari, agungyappi Agus Akhtawan Andhi Yanto Wibowo Arfiani Nur Khusna Asno Azzawagama Firdaus Caesar Irjayana, Rizky Dedi Wijayanti Deni Kuswandani Dianda Rifaldi Febriyanto, Bayu Herman Imam Riadi Kencana, Nagala Wangsa Muhammad Kunta Biddinika Nadia Wati Aprianti Nilam Tri Astuti Prambudi, Rizal Prambudi Putri, Salsabilla Azahra Rinday Zildjiani Salji Rusydi Umar Salsabilla Azahra Putri Sunardi Suyahman Suyahman