PENGENALAN RAMBU LALU LINTAS MENGGUNAKAN METODE YOLOV8

  • MUH. IKBAL Universitas HaluOleo
  • Rizal Adi Saputra Halu Oleo University
DOI: https://doi.org/10.31000/jika.v8i2.10609

Abstract

Teknologi kendaraan otonom telah mengalami perkembangan pesat dalam beberapa tahun terakhir. Tujuan utama dari teknologi ini adalah untuk mengurangi kecelakaan lalu lintas dan menciptakan lingkungan berkendara yang lebih aman. Kendaraan otonom diharapkan dapat sepenuhnya memahami lingkungan lalu lintas dan beroperasi sesuai aturan. Salah satu metode kecerdasan buatan yang sering digunakan dalam teknologi ini adalah deep learning YOLO. Penelitian ini mengembangkan metode deteksi jenis rambu lalu lintas menggunakan YOLO V8, yang merupakan pengembangan dari metode CNN. Data yang digunakan dalam penelitian ini mencakup 30 label jenis rambu lalu lintas dengan total 4650 citra. Dataset dibagi dengan proporsi 70:30, di mana 70% digunakan untuk pelatihan dan 30% untuk pengujian. Setelah pembagian, dataset pelatihan terdiri dari 3232 gambar, sementara dataset pengujian mencakup 1418 gambar. Hasil pengujian menunjukkan bahwa model ini memiliki kinerja yang baik, dengan nilai Precision sebesar 0,993, Recall sebesar 0,999, mAP50 sebesar 0,995, dan mAP50-95 sebesar 0,984. Nilai-nilai ini menunjukkan bahwa model ini mampu mengidentifikasi dan mengklasifikasikan rambu lalu lintas dengan akurasi yang tinggi.

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Published
2024-04-30
Issue
Vol. 8 No. 2 (2024): JIKA (Jurnal Informatika)
Section
Articles

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