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Penelitian ini bertujuan untuk merumuskan dan menilai kerangka deteksi rambu lalu lintas real-time dalam konteks di negara Indonesia, menggunakan YOLOv11. Mengingat sifat heterogen rambu-rambu lalu lintas dan kondisi jalan raya di Indonesia, ada kebutuhan mendesak untuk model yang tangguh dan tepat guna meningkatkan keselamatan berkendara. Metode yang digunakan adalah YOLOv11, sebuah algoritma deep learning yang dirancang untuk deteksi objek dengan kecepatan tinggi dan akurasi tinggi. Algoritma ini memproses gambar dalam satu langkah inferensi dan memiliki arsitektur yang dioptimalkan untuk pengenalan real-time. Proses meliputi preprocessing data seperti augmentasi dan anotasi, pelatihan model menggunakan dataset rambu lalu lintas Indonesia, serta evaluasi performa model menggunakan metrik precision, recall, dan mean average precision (mAP). Temuan menunjukkan bahwa model berhasil mencapai mAP 0,99, dengan akurasi tinggi di berbagai klasifikasi rambu lalu lintas. Evaluasi menggunakan Confusion Matrix menunjukkan tingkat kesalahan yang dapat diabaikan, menandakan keandalan model untuk aplikasi dunia nyata. Aplikasi potensial teknologi ini sangat penting dalam memperkuat sistem keselamatan berkendara dan transportasi cerdas di Indonesia.

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YOLOv11 Real-time Indonesia Rambu-rambu Lalu lintas YOLOv11 Real-time Indonesia Signs Traffic

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Artikel ini berlisensi Creative Commons Attribution-NoDerivatives 4.0 International License.

Cara Mengutip
[1]
A. I. Pradana, H. Harsanto, dan W. Wijiyanto, “Deteksi Rambu Lalu Lintas Real-Time di Indonesia dengan Penerapan YOLOv11: Solusi Untuk Keamanan Berkendara”, Jurnal Algoritma, vol. 21, no. 2, hlm. 145–155, Nov 2024.
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