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      "title": "DETEKSI OBJEK PERLENGKAPAN KEAMANAN HELM\r\nPENGENDARA MOTOR MENGGUNAKAN YOLOV8",
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      "abstract": "Pelanggaran penggunaan helm masih menjadi tantangan besar dalam\r\nkeselamatan lalu lintas, sering kali disebabkan oleh rendahnya kesadaran\r\npengendara dan minimnya kehadiran petugas kepolisian di titik-titik terpencil.\r\nKondisi ini meningkatkan risiko cedera fatal akibat kecelakaan pada infrastruktur\r\njalan desa yang cenderung minim pengawasan. Mengingat keterbatasan personel\r\nuntuk melakukan pemantauan manual secara luas dan konsisten, diperlukan solusi\r\nteknologi yang efisien dan mandiri. Penelitian ini bertujuan untuk mengembangkan\r\nmodel deteksi penggunaan helm menggunakan algoritma YOLOv8 berbasis video\r\nsebagai sistem otomatisasi pengawasan lalu lintas. Metode penelitian meliputi\r\npengumpulan dan anotasi dataset citra pengendara motor, pelatihan model\r\nmenggunakan arsitektur YOLOv8, evaluasi performa dengan metrik accuracy,\r\nprecision, recall, dan mAP, serta implementasi model dalam aplikasi berbasis\r\nStreamlit untuk visualisasi hasil deteksi model secara daring. Hasil penelitian\r\nmenunjukkan bahwa model mampu mendeteksi penggunaan helm dengan tingkat\r\nakurasi yang baik dan performa deteksi yang cukup baik pada berbagai kondisi\r\npencahayaan dan sudut pengambilan gambar. Implementasi sistem ini dapat\r\ndimanfaatkan oleh otoritas lokal, maupun pihak terkait sebagai alat bantu\r\npemantauan pelanggaran secara otomatis. Penelitian ini juga membuka peluang\r\npengembangan lebih lanjut melalui peningkatan jumlah dataset, optimasi performa\r\nmodel, dan integrasi dengan sistem pengawasan berbasis CCTV secara langsung.",
      "eprint_status": "archive",
      "metadata_visibility": "show",
      "department": "Fakultas Ilmu Komputer"
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