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All Journal Jurnal Edukasi Elektro Jurnal Teliska Jurnal Ampere Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Profesi Insinyur Universitas Lampung Aptekmas : Jurnal Pengabdian Kepada Masyarakat JatiRenov: Jurnal Aplikasi Teknologi Rekayasa dan Inovasi JSSIT: Jurnal Sains dan Sains Terapan Jurnal Sains dan Sains Terapan
Muhammad Amri Yahya
Politeknik Negeri Sriwijaya
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Pengembangan Model Deteksi Sampah Berbasis YOLOV8 Dan Evaluasi Performanya Dalam Sistem Monitoring Lingkungan Sungai Ramadhoni, M.; Husni, Nyayu Latifah; Muhammad Amri Yahya
Jurnal Profesi Insinyur Universitas Lampung Vol. 6 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpi.v6n2.258

Abstract

Pencemaran sungai akibat sampah merupakan permasalahan lingkungan yang membutuhkan solusi berbasis teknologi. Penelitian ini bertujuan untuk mengembangkan model deteksi sampah di permukaan sungai menggunakan algoritma YOLOv8 serta mengevaluasi performanya dalam sistem monitoring lingkungan. Dataset citra sampah dilabeli menggunakan Roboflow dan dilatih menggunakan YOLOv8 di Google Colaboratory. Model diuji dengan parameter pelatihan sebanyak 50 epoch, ukuran citra 320 × 320 piksel, dan batch size 32. Hasil pelatihan menunjukkan nilai precision sebesar 0.894, recall 0.833, mAP50 sebesar 0.89, dan mAP50-95 sebesar 0.726. Evaluasi lanjutan melalui confusion matrix dan pengujian terhadap 20 citra acak menunjukkan model mampu mendeteksi objek sampah dengan akurasi dan stabilitas yang baik dalam berbagai kondisi citra. Dengan demikian, model ini dinilai layak untuk diimplementasikan sebagai bagian dari sistem monitoring lingkungan sungai secara otomatis dan real-time.
YOLOv9-Assisted Vision System for Health Assessment in Poultry Using Deep Neural Networks Risma, Pola; Prasetyo, Tegar; Muhammad Amri , Yahya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026 (Article in Progress)
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, advancing predictive analytics and welfare-oriented poultry farming.
Co-Authors A. Rahman Abda Abda Abi Burhan Adha, Ufairi Africano, Fernando Agum Try Wardhana Aldila Sari Utami Ali Muhtar BillY Dewantara Desiana, Lidia Dyah Utari Yusa Wardhani Ebtaria Nadeak, Ebtaria Eka Putri Manurung, Nancy Evelina Evelina Fredi Prima Sakti Gemala Cahya Hermialingga, Septi Imelda, Anggun Manurung, Nancy Eka Putri Muhammad Fikri Nancy Eka Putri Manurung Nugraha, Yoga Aji Nyayu Latifah Husni, Nyayu Latifah Pola Risma Prasetyo, Tegar Putri Manurung, Nancy Eka Ramadhoni, M. Renny Maulidda Sari Utami, Aldila Septi Hermialingga Tegar Prasetyo