Article Per Year (5 Year)
p-Index From 2021 - 2026
2.083
P-Index
This Author published in this journals
All Journal Jurnal Sarjana Teknik Informatika PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer IJISTECH (International Journal Of Information System & Technology) JOISIE (Journal Of Information Systems And Informatics Engineering) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Tunas IJISTECH JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Journal of Computers for Society
Murien Nugraheni
Universitas Negeri Jakarta
Arts Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Convolutional neural network DenseNet in classifying dyslexic handwriting images Chelsea Zaomi Pondayu; Widodo Widodo; Murien Nugraheni
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp220-232

Abstract

Dyslexia is a specific learning disability (SLD) associated with word-level reading difficulties and often manifests in childhood handwriting through irregular spacing and inconsistent letter sizing, due to shared phonological and orthographic processing. Early identification is critical; however, traditional diagnostic procedures are time-consuming and unsuitable for large-scale screening. This study aimed to develop a handwriting analysis at the paragraph-level using a DenseNet121 convolutional neural network (CNN) model as a low-cost dyslexia screening tool for resource-constrained educational settings. One hundred English handwriting images were preprocessed and standardized into two hundred samples, with 70% of the dataset evaluated using 4-fold cross-validation and the remaining 30% used for testing. The model achieved 90% test accuracy and 92.86% training accuracy, significantly outperforming a random forest baseline that reached 83.57% train accuracy and 63.33% test accuracy, with statistical significance confirmed by McNemar’s test. The main contribution of this study is the demonstration that a lightweight, single-architecture DenseNet121 using paragraph-level analysis can achieve competitive performance compared to prior studies that relied on more complex hybrid models and character-level analysis, while requiring substantially lower computational resources and simplified pipeline. These findings indicate that DenseNet121 provides a robust and low-cost solution for preliminary dyslexia screening in resource-limited educational environments.
Co-Authors Ali Idrus Ali Idrus Chelsea Zaomi Pondayu Diat Nurhidayat Dodi Saputra Egan Alif Zanetti Fahrurrozi Fahrurrozi Fryda Fatmayati Hamidillah Ajie Hamidillah Ajie Ika Arfiani Lipur Sugiyanta Lipur Sugiyanta M. Hadi Prayitno Muchammad Ficky Duskarnaen Muhammad Adryan Hartono Muhammad Dzikrullah Suratin Olifvia Afrillazhary Rini Nuraini Rizky Febrian Rosa Aulia Nisa Syalva Syadila Vienda Miccela Seldry Widodo Zahwa Putri Rahmayani