Pengenalan Tulisan Tangan Per Kata Menggunakan Metode Convolutional Neural Network
DOI:
https://doi.org/10.29407/004vnt54Keywords:
citra digital, convolutional neural network, pengenalan tulisan tangan, tulisan tangan per kataAbstract
Tulisan tangan masih banyak digunakan dalam berbagai dokumen sehingga diperlukan sistem pengenalan yang mampu membaca tulisan tangan secara otomatis dan akurat. Penelitian ini bertujuan untuk mengembangkan sistem pengenalan tulisan tangan per kata menggunakan metode Convolutional Neural Network (CNN) berbasis pengolahan citra digital. Proses penelitian diawali dengan pengumpulan dataset citra tulisan tangan, dilanjutkan dengan tahap preprocessing yang meliputi konversi grayscale, normalisasi, dan perubahan ukuran citra. Setiap citra kata digunakan sebagai data masukan untuk proses pelatihan model CNN. Model yang telah dilatih kemudian diuji untuk mengukur tingkat akurasi pengenalan kata tulisan tangan. Implementasi sistem dilakukan dalam bentuk aplikasi berbasis web yang memungkinkan pengguna mengunggah citra tulisan tangan dan memperoleh hasil prediksi kata secara otomatis. Hasil pengujian menunjukkan bahwa metode CNN mampu mengenali tulisan tangan per kata dengan tingkat akurasi yang baik pada data uji. Penelitian ini menunjukkan bahwa pendekatan CNN efektif digunakan dalam sistem pengenalan tulisan tangan dan berpotensi diterapkan pada berbagai kebutuhan pengolahan dokumen berbasis citra.
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Copyright (c) 2026 Muhammad Arief Pramana, Muhammad Wahyu Rifa'i
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