Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal INOVTEK Polbeng - Seri Informatika
Azizah Fauni Saputri
Informatics Department, University of Riau
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of Handwritten Digit Recognition Using CNN in an Augmented Reality-Based Geometry Learning Application Azizah Fauni Saputri; Feri Candra
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wfjqak08

Abstract

Instruction on 3D shapes in elementary schools is still dominated by the use of two-dimensional media, making it difficult for students to visualise three-dimensional objects. Furthermore, most Augmented Reality (AR)-based learning applications focus primarily on presenting learning materials without providing an assessment mechanism that supports handwritten student answers. This study aims to implement Handwritten Digit Recognition (HDR) based on a Convolutional Neural Network (CNN) in an AR-based 3D-shape learning application. The proposed approach integrates CNN-based handwritten digit recognition as an evaluation mechanism within the AR-based learning application, allowing students' handwritten answers to be automatically recognised and evaluated. The CNN model utilises the LeNet-5 architecture and was trained using a combined dataset consisting of 70,000 MNIST images and 10,000 handwritten images independently collected by the researcher. The testing results show that the model achieved an accuracy of 98.32%, while HDR testing within the application using 250 samples of student handwriting achieved an accuracy of 96.80%. The results indicate that the implementation of CNN-based HDR in an AR-based 3D shape learning application can recognise handwritten digits with high accuracy and provide an automated learning evaluation mechanism based on handwritten answers.
Co-Authors Feri Candra