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All Journal Kalbiscientia Jurnal Sains dan Teknologi
Edelbert Strago Giamiko
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Industrial & Manufacturing Engineering Mechanical Engineering

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Pengembangan Aplikasi Pengenalan Tulisan Tangan Abjad dan Angka Berbasis Convolutional Neural Network Edelbert Strago Giamiko; Tjiong, Edwin
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 11 No. 02 (2024): Jurnal Sains dan Teknologi
Publisher : Research and Community Service INSTITUT TEKNOLOGI DAN BISNIS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v11i02.3626

Abstract

This research aims to make an application that recognizes and predicts handwritings of alphabets and numbers using Convolutional Neural Network (CNN). This application uses an incremental model with 2 steps for its development. The data used is EMNIST dataset, images of handwritten letters consists of Roman capital letters, Roman small letters, and Arabic numerals (0-9) that are split into 47 different classes. The model and application successfully predicted handwritings of alphabets and numbers with an average precision percentage of 76,24%.
Pengembangan Aplikasi Pengenalan Tulisan Tangan Abjad dan Angka Berbasis Convolutional Neural Network Edelbert Strago Giamiko; Tjiong, Edwin
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 11 No. 02 (2024): Jurnal Sains dan Teknologi
Publisher : Research and Community Service UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v11i02.3626

Abstract

This research aims to make an application that recognizes and predicts handwritings of alphabets and numbers using Convolutional Neural Network (CNN). This application uses an incremental model with 2 steps for its development. The data used is EMNIST dataset, images of handwritten letters consists of Roman capital letters, Roman small letters, and Arabic numerals (0-9) that are split into 47 different classes. The model and application successfully predicted handwritings of alphabets and numbers with an average precision percentage of 76,24%.
Co-Authors Tjiong, Edwin