IDENTIFIKASI JENIS KENDARAAN BERMOTOR DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORKS
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Abstract
Deteksi jenis kendaraan bermotor memainkan peran sentral dalam pengaturan lalu lintas, penegakan hukum, keamanan, dan sistem transportasi pintar. Dengan kemampuan luar biasa dalam mendeteksi dan mengklasifikasikan kendaraan dengan akurat, pihak berwenang dapat mengoptimalkan waktu sinyal lalu lintas, pengelolaan jalur, dan aliran lalu lintas secara efisien. Deteksi jenis kendaraan juga memberikan dukungan penting dalam penegakan peraturan lalu lintas dan memverifikasi kepatuhan kendaraan terhadap batasan tertentu, termasuk jalur kendaraan bersama, tol, dan peraturan parkir. Di sisi keamanan, teknologi ini berperan krusial dalam mengidentifikasi kendaraan mencurigakan, mencegah ancaman, dan meningkatkan keselamatan di area sensitif. Salah satu pendekatan populer dalam mendukung sistem deteksi jenis kendaraan bermotor otomatis adalah menggunakan algoritma deep learning, khususnya Convolutional Neural Network (CNN). Dengan kemampuannya mengenali pola dan fitur pada citra kendaraan menggunakan struktur jaringan syaraf tiruan, CNN mampu memberikan hasil yang luar biasa. Penelitian ini bertujuan mengembangkan sistem otomatis deteksi jenis kendaraan bermotor dengan menggunakan algoritma CNN. Hasil penelitian menunjukkan kinerja yang sangat baik, dengan rata-rata presisi sebesar 97,00%, sensitivitas/recall sebesar 97,60%, spesifisitas sebesar 97,59%, dan akurasi sebesar 97,30%.
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