Deteksi Dini Disleksia Berbasis Analisis Tulisan Tangan Menggunakan Transfer Learning MobileNetV2
DOI:
https://doi.org/10.22441/incomtech.v16i1.36696Kata Kunci:
Dyslexia, Convolutional Neural Network, MobileNetV2, Transfer Learning,Abstrak
Keterlambatan dalam mengidentifikasi disleksia dapat secara signifikan memengaruhi perkembangan akademik serta emosional anak, sebuah tantangan yang di Indonesia masih sering dihadapi akibat ketergantungan pada metode observasi manual. Untuk mengatasi masalah tersebut, sebuah prototipe aplikasi berbasis web dirancang sebagai alat bantu untuk mengidentifikasi potensi disleksia secara dini melalui analisis pola tulisan tangan. Sistem ini diimplementasikan menggunakan algoritma Convolutional Neural Network (CNN) dengan pendekatan transfer learning pada arsitektur MobileNetV2. Model dilatih menggunakan dataset primer yang terdiri dari 75 citra tulisan tangan siswa dari SD Negeri Klegenwonosari, Kabupaten Kebumen yang diperkaya variasinya melalui teknik augmentasi data. Evaluasi model melalui K-Fold Cross-Validation menunjukkan capaian akurasi rata-rata yang menjanjikan sebesar 0,7200±0,0980. Penambahan volume data terbukti secara positif memengaruhi stabilitas kinerja model serta kemampuannya untuk mulai mengenali kelas minoritas. Prototipe aplikasi ini diharapkan dapat berfungsi sebagai alat skrining awal yang praktis bagi para pendidik dan orang tua.Unduhan
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