Pengklasifikasian Citra Tulisan Anak Melalui Metode CNN sebagai Pendukung Pendeteksian Dini Disgrafia
DOI:
https://doi.org/10.22441/incomtech.v11i3.13769Kata Kunci:
disgrafia, CNN, machine learningAbstrak
Era digitalisasi tidak membuat kegiatan menulis dengan tangan dilupakan, karena kegiatan tersebut dibutuhkan untuk berkomunikasi secara tertulis dalam pengoptimalan fungsi otak dan termasuk elemen penting dalam pendidikan anak usia dini. Disgrafia merupakan gangguan belajar yang berpengaruh pembentukan huruf, spasi, ejaan, dan kecepatan menulis. Gangguan disgrafia yang tidak terdeteksi secara dini, berdampak pada anak dan lingkungan keluarganya yang cenderung terintimidasi dan frustasi. Beberapa negara memiliki peningkatan penderita disgrafia. Pemeriksaan metode konvensional memiliki keterbatasan dalam waktu dan biaya, dimana seorang asesor harus mengevaluasi dan memantau anak penderita disgrafia secara intensif. Convolutional Neural Network merupakan subdomain Deep Learning yang efektif dalam pengenalan objek gambar. Model 4 lapisan convolution dengan fungsi aktivasi ReLU Dengan rasio 80% data training: 20% data testing dengan 50 epoch, tingkat keakurasian mencapai 97%. Pemeriksaan dini disgrafia dapat membantu perbaikan kemampuan komunikasi verbal menulis anak. Siswa penderita disgrafia dapat mencapai kapasitas maksimal akademik dan menjadi orang sukses dengan bantuan dan dukungan pembelajaran yang tepat.Unduhan
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