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<note>Penelitian ini bertujuan untuk mengembangkan sistem segmentasi objek manusia dari background dalam video menggunakan arsitektur Attention U-Net, yang kemudian mengubah background menjadi greenscreen. Dalam penelitian ini digunakan beberapa metode, seperti kuesioner untuk mengumpulkan data permasalahan, metode komparatif untuk membandingkan model dengan dropout dan tanpa dropout, dan metode eksperimen dalam mencari kombinasi dropout terbaik. Penelitian ini menghasilkan sebuah sistem simulasi web untuk melakukan pergantian background pada video. Hasil dari pengolahan data selama penelitian menunjukan bahwa penggunaan framework Tensorflow untuk segmentasi menggunkan arsitektur Attention U-Net pada video memiliki hasil terbaik pada batch size 32 dimana dari 226 frame video yang diproses rata-rata nilai IoU mencapai 96.72%, Precision 96.78%, Recall 99.94%, dan F1 Score sebesar 98.33%. Kemudian mekanisme dropout terbukti dapat mengurangi overfitting dari 11.90% menjadi 6.57% pada IoU, dan 8.53% menjadi 4.97% pada F1 Score selama pelatihan model. Kata kunci:
video, segmentasi, background, greenscreen, attention U-Net, dropout, overfitting</note>
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