Optimasi Akurasi Deteksi Sikap Tangan menggunakan metode Convolutional Neural Network

Jidan Dewa Saksana, Ema Utami, Hanif Al Fatta

Abstract


Penelitian ini akan menggunakan pendekatan berbasis deep learning yang telah terbukti berhasil dalam deteksi sikap tangan, yakni Convolutional Neural Networks (CNN). Studi mengusulkan metode optimasi yang mencakup peningkatan preprocessing data, arsitektur CNN yang disesuaikan, dan strategi augmentasi data untuk meningkatkan model terhadap variasi input. Hasil eksperimen menunjukkan bahwa metode optimasi yang diusulkan berhasil meningkatkan akurasi deteksi gerakan tangan dibandingkan dengan pendekatan standar. Pengujian dilakukan pada dataset publik dengan metrik evaluasi yang berfokus pada akurasi

Keywords


Convolutional Neural Network Deep Learning; Hands Gesture Recognition; Image Processing

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DOI: https://doi.org/10.29100/jipi.v10i4.6735

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