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Abstract

Penelitian ini membandingkan efektivitas algoritma YOLOv5 dan YOLOv8 dalam mendeteksi objek seperti manusia, hewan, dan tumpukan hewan dalam sistem pemeliharaan berbasis AI. Dataset yang digunakan terdiri dari 5.000 gambar berbasis anotasi, dengan data untuk 70% penelitian, 20% untuk validasi, dan 10% untuk pengujian. Model dievaluasi menggunakan metrik precision, recall, mAP0.5, dan mAP0.5:0.95 setelah 50 epoch. Hasilnya menunjukkan bahwa YOLOv8 memiliki performa keseluruhan yang lebih baik daripada YOLOv5, terutama pada metrik mAP0.5:0.95 dengan peningkatan sebesar 25%, sedangkan YOLOv5 hanya mencapai 10%. Dalam hal mendeteksi objek yang kompleks, YOLOv8 lebih akurat daripada YOLOv5, yang lebih cepat tetapi kurang sensitif terhadap objek yang lebih kecil. Hasilnya, YOLOv8 lebih cocok untuk aplikasi pendeteksi lingkungan secara real-time, meningkatkan sampah yang berkelanjutan, dan berintegrasi dengan konsep smart city.

Keywords

YOLOv5 YOLOv8 Deteksi Objek Pemantauan Lingkungan Kota Pintar

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Copyright (c) 2025 Deden Lasmana, Nyayu Latifah Husn, RD. Kusumanto

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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