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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Elektron Jurnal Ilmiah Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Sriwijaya Electrical and Computer Engineering (Selco) Journal TECHNOLOGIC
Pola Risma
Politeknik Negeri Sriwijaya
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

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YOLOv9-Assisted Vision System for Health Assessment in Poultry Using Deep Neural Networks Pola Risma; Tegar Prasetyo; Yahya Muhammad Amri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2414

Abstract

Poultry farming represents one of the fastest-growing sectors in global food production, yet disease outbreaks, high mortality, and labor shortages continue to threaten its sustainability. Conventional health monitoring methods based on visual inspection are time-consuming, subjective, and inadequate for early anomaly detection. In response, computer vision and deep learning have emerged as transformative tools for livestock management. While prior implementations of the YOLO object detection family, such as YOLOv5 and YOLOv8, have achieved notable success, their performance often deteriorates in dense flocks, low-light conditions, and occlusion-prone environments. This study introduces a YOLOv9-assisted vision framework tailored for poultry health assessment in commercial farm settings. The system integrates smart cameras with edge computing to enable real-time detection of behavioral and physiological anomalies without dependence on high-bandwidth or cloud-based resources. A dataset of 903 annotated poultry images, categorized into healthy and sick classes, was employed for model development. The trained model achieved 88.7% precision, 97% recall, an F1-score of 0.82, and a mAP@0.5 of 0.88, demonstrating robustness under variable illumination, bird occlusion, and high-density environments. Comparative evaluation confirmed that YOLOv9 provides a superior balance of accuracy, generalization, and computational efficiency relative to YOLOv8–YOLOv11, supporting practical deployment on edge devices. Limitations include the binary scope of health classification and reliance on a single dataset. Future directions involve extending the framework to multi-class disease recognition, cross-dataset validation, behavior-based temporal modeling, and multimodal fusion, thereby advancing predictive analytics and welfare-oriented poultry farming.
Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya Angga Liwijaya; Pola Risma; Yurni Oktarina; Tresna Dewi
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/pvdpsr36

Abstract

Accurate prediction of solar irradiance plays a critical role in the planning and operation of renewable energy systems, particularly for photovoltaic integration and energy management. This study investigates the use of a deep learning approach based solely on Convolutional Neural Networks (CNN) to forecast short-term solar irradiance values. The model is trained using normalized multivariate time series data, which include several meteorological parameters as input features. The CNN architecture is designed to extract temporal patterns from the input sequences and predict radiation intensity at the next time step. Experimental results show that the proposed model achieves strong predictive performance, with a Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.0242, Mean Absolute Error (MAE) of 0.0184, and a coefficient of determination (R²) of 0.9607. These findings demonstrate that CNN, despite its simplicity, is capable of effectively learning complex temporal relationships in solar irradiance data. Furthermore, the loss curves for both training and validation sets indicate stable convergence without signs of overfitting. The results suggest that CNN-based forecasting models can offer a lightweight and accurate solution for real-time solar prediction applications, especially when computational resources are limited.
Co-Authors Adella Rialita Agung Tantowi Junior Ahmad Fudholi Amperawan Amperawan Andika Wijaya, Andika Angga Liwijaya Anggraini, Citra Arbi Nugraha Ariski, Ariski Sapni Putra Assyifa Mourlina Faraquinsha Baiki, Amaldi Bambang, Muhammad Refo Bimo, Muhammad Citra Anggraini Citra Anggraini Dadi Setiadi Dewi Permata Sari Dicky Astra Yudha Dira, Arbi Pratam Edo Triyandi Evelina Ginting Fairuz Attalah Hendra Marta Yuda Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah Islami, Miftahu Khoiri Lukman Nul Hakim Lutfi, Iskandar M. Taufik Roseno Mardianto, Yudhi Masayu Anisah Mayastri Devana Muhammad Bagaskara Muhammad Billy Akbar Muhammad Dede Yusuf Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Muslimin, Selamat Nur Aina Okta Ferrisa Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Putri Repina Kesuma R. D. Kusumanto Rahman, Raihan Aldiaz Rapli Wijaya Ray, Ammar Dion RD Kusumanto RD Kusumanto RD Kusumanto Renny Maulidda, Renny Riyo Irawan Roseno, M. Taufik Rusdianasari Rusdianasari Sakuraba, Takahiro Sari Kartini SELAMET MUSLIMIN Septriani, Dela Siti Afiyah Qatrunnada Siti Nurmaini Solly Aryza Sonny Syahputra Syahrian, Nur Mutiara Tampubolon, Debora Tegar Prasetyo Tresna Dewi Tresna Dewi Tresna Dewi Tresna Dewi Tresna Dewi Try Rahmany Fajriah Wijanarko, Yudi Yahya Muhammad Amri Yudi Wijanarko Yudi Wijanarko, Yudi Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Zarqa Mulya