IMPLEMENTASI MODEL YOLO V5 UNTUK DETEKSI KOREK API DALAM KEAMANAN PENERBANGAN

Rizki Hesananda

doi:10.23960/jitet.v13i1.5553

Rizki Hesananda
Universitas Siber Indonesia

DOI: https://doi.org/10.23960/jitet.v13i1.5553

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Abstract

Keamanan dalam transportasi udara merupakan prioritas utama, dengan regulasi ketat terkait barang-barang yang dapat dibawa ke dalam pesawat. Korek api adalah salah satu barang yang sering kali dibatasi karena potensinya sebagai sumber api yang berbahaya. Penelitian ini bertujuan mengembangkan model deteksi korek api yang akurat menggunakan YOLO v5 untuk meningkatkan keamanan penerbangan. Metodologi yang digunakan dalam penelitian ini adalah CRISP-DM (Cross Industry Standard Process for Data Mining), yang terdiri dari tahapan pemahaman bisnis, pemahaman data, persiapan data, pemodelan, dan evaluasi. Dataset yang digunakan adalah data primer yang terdiri dari 37 gambar potret korek api dengan resolusi tinggi, yang diambil sendiri menggunakan kamera HP. Gambar-gambar ini diunggah ke platform Roboflow untuk anotasi dan augmentasi, menghasilkan dataset yang tiga kali lipat dari jumlah gambar awal. Model YOLO v5 dilatih menggunakan Google Colab dengan 100 epoch, batch size 16, dan ukuran gambar 416 piksel. Evaluasi model menunjukkan hasil yang sangat baik dengan nilai precision, recall, mAP 0,5, dan mAP 0,95 yang meningkat mendekati 1 seiring bertambahnya epoch. Hasil ini membuktikan bahwa YOLO v5 memiliki kemampuan deteksi korek api yang sangat akurat, yang dapat diimplementasikan dalam sistem keamanan bandara untuk mendeteksi barang-barang berbahaya secara otomatis. Penelitian ini menyimpulkan bahwa model YOLO v5 efektif untuk deteksi korek api dan dapat meningkatkan keselamatan transportasi udara.

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