Pengenalan Gambar Untuk Pelatihan Bicara Bagi Anak Dengan Speech Delay Menggunakan Convolutional Neural Network
https://doi.org/10.26594/teknologi.v15i2.5790
Abstract
Penelitian ini bertujuan untuk membangun dan mengevaluasi model Convolutional Neural Network (CNN) dalam mengklasifikasikan gambar coretan tangan berupa angka dan bentuk bangun datar. Dataset yang digunakan merupakan gabungan dari MNIST dan Google QuickDraw, dengan total 16 kelas. Model CNN yang dikembangkan terdiri dari empat blok konvolusi dan dua fully connected layer, dilatih selama 30 epoch menggunakan teknik regularisasi dan normalisasi untuk meningkatkan performa generalisasi. Hasil pelatihan menunjukkan akurasi validasi tertinggi sebesar 98,61% dan loss minimum sebesar 0,1185. Evaluasi menggunakan metrik precision, recall, dan f1-score menunjukkan rata-rata nilai sebesar 97,71%, 98,22%, dan 98,75%. Model ini menunjukkan kinerja tinggi pada kelas angka maupun bentuk bangun datar, meskipun terdapat tantangan pada bentuk visual yang kompleks dan serupa. Dengan akurasi dan konsistensi klasifikasi yang tinggi, model CNN ini dinilai layak sebagai komponen utama dalam sistem bantu pembelajaran visual untuk anak dengan keterlambatan bicara (speech delay).
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