Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Abdul Fadlil
Universitas Ahmad Dahlan,Yogyakarta, Indonesia
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character Egi Dio Bagus Sudewo; Muhammad Kunta Biddinika; Abdul Fadlil
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3855

Abstract

This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese
DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character Egi Dio Bagus Sudewo; Muhammad Kunta Biddinika; Abdul Fadlil
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3855

Abstract

This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese
Co-Authors Egi Dio Bagus Sudewo Muhammad Kunta Biddinika