DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character
DOI:
https://doi.org/10.30812/matrik.v23i2.3855Keywords:
Convolution Neural Network, Character Recognition, Cultural Heritage Preservation, Densenet, Javanese Hanacaraka ScriptAbstract
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
Downloads
Download data is not yet available.
References
[1] F. K. Sari, “The Local Wisdom in Javanese Thinking Culture,†Diksi, vol. 28, no. 1, pp. 86–100, 2020.
[2] J. Sukoyo, E. S. Utami, and E. Kurniati, “The Development of Montessori-Based Javanese Script Learning Model,†Proceedings
of the 2nd International Conference on Innovation in Education and Pedagogy (ICIEP 2020), vol. 619, no. Iciep 2020, pp. 99–
102, 2022.
[3] J. D. Kelleher, The Deep Learning, 2019.
[4] A. Dhillon and G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object
detection,†Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85–112, 2020, doi: 10.1007/s13748-019-00203-0.
[5] N. P. Sutramiani, N. Suciati, and D. Siahaan, “Transfer Learning on Balinese Character Recognition of Lontar Manuscript Using
MobileNet,†ICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences, pp. 0–4,
2020, doi: 10.1109/ICICoS51170.2020.9299030.
[6] S. K. C and C. Nattee, “Handwritten Alphanumeric Character Recognition,†vol. 12, no. 1, pp. 20–30, 2023, doi:
10.5072/FK26H4PV9J.2023.05.08.001.
[7] V. Madane, K. Ovhal, and M. Bhong, “Handwriting Recognition Using Artificial Intelligence Neural Network and Image
Processing,†International Research Journal of Modernization in Engineering Technology and Science, no. 03, pp. 1432–1447,
2023, doi: 10.56726/irjmets34395.
[8] S. F. M¨uller-Cleve, V. Fra, L. Khacef, A. Peque˜no-Zurro, D. Klepatsch, E. Forno, D. G. Ivanovich, S. Rastogi, G. Urgese,
F. Zenke, and C. Bartolozzi, “Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic
hardware,†Frontiers in Neuroscience, vol. 16, 2022, doi: 10.3389/fnins.2022.951164.
[9] U. BS and U. K, “a Review Paper on Ocr Using Convolutional Neural Networks,†International Journal of Engineering Applied
Sciences and Technology, vol. 7, no. 7, pp. 102–106, 2022, doi: 10.33564/ijeast.2022.v07i07.018.
[10] M. F. Muhdalifah, “Pooling Comparison in CNN Architecture for Javanese Script Classification,†International Journal of
Informatics and Computation, vol. 3, no. 2, p. 15, 2022, doi: 10.35842/ijicom.v3i2.30.
[11] A. Budiman, A. Fadlil, and R. Umar, “Identification of Learning Javanese Script Handwriting Using Histogram Chain Code,â€
EDUMASPUL: Jurnal Pendidikan, vol. 7, no. 1, pp. 147–153, 2023.
[12] M. A. Rasyidi, T. Bariyah, Y. I. Riskajaya, and A. D. Septyani, “Classification of handwritten javanese script using random
forest algorithm,†Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, pp. 1308–1315, 2021, doi:
10.11591/eei.v10i3.3036.
[13] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,†Evolutionary
Intelligence, vol. 15, no. 1, pp. 1–22, 2022, doi: 10.1007/s12065-020-00540-3.
[14] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, A survey of the recent architectures of deep convolutional neural networks.
Springer Netherlands, 2020, vol. 53, no. 8.
[15] Purwono, A. Ma’arif, W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky, and Q. M. U. Haq, “Understanding of Convolutional
Neural Network (CNN): A Review,†International Journal of Robotics and Control Systems, vol. 2, no. 4, pp. 739–748,
2022, doi: 10.31763/ijrcs.v2i4.888.
[16] S. Singh and D. Schicker, “Seven Basic Expression Recognition Using ResNet-18,†pp. 1–3, 2021.
[17] D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,†2019 IEEE 10th Annual Ubiquitous
Computing, Electronics and Mobile Communication Conference, UEMCON 2019, pp. 0280–0285, 2019, doi: 10.1109/UEMCON47517.2019.8993089.
[18] D. Gupta, A. Gupta, R. Gandhi, P. Vishwavidyalaya, and S. S. Gupta, “Identification of Alzheimer’s disease from MRI image
employing a probabilistic deep learning-based approach and the VGG16,†2023.
[19] W. Gong, X. Zhang, B. Deng, and X. Xu, “Palmprint recognition based on convolutional neural network-alexnet,†Proceedings
of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, vol. 18, pp. 313–316, 2019,
doi: 10.15439/2019F248.
[20] N. S. Shadin, S. Sanjana, and N. J. Lisa, “COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(
CNN) and InceptionV3,†2021 International Conference on Information Technology, ICIT 2021 - Proceedings, vol. 3, no.
September 2012, pp. 799–804, 2021, doi: 10.1109/ICIT52682.2021.9491752.
[21] G. S. Nugraha, M. I. Darmawan, and R. Dwiyansaputra, “Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16,
Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition,†Kinetik: Game Technology, Information System, Computer
Network, Computing, Electronics, and Control, vol. 4, no. 2, 2023, doi: 10.22219/kinetik.v8i2.1667.
[22] Z. Zhong, M. Zheng, H. Mai, J. Zhao, and X. Liu, “Cancer image classification based on DenseNet model,†Journal of Physics:
Conference Series, vol. 1651, no. 1, 2020, doi: 10.1088/1742-6596/1651/1/012143.
[23] G. Huang, Z. Liu, G. Pleiss, L. Van Der Maaten, and K. Q. Weinberger, “Convolutional Networks with Dense Connectivity,â€
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 8704–8716, 2022, doi:
10.1109/TPAMI.2019.2918284.
[24] Z. Shi, M. Chen, and Z. Wu, “Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel
Attentional Feature Fusion,†Electronics (Switzerland), vol. 12, no. 13, 2023, doi: 10.3390/electronics12132991.
[25] S. A. Albelwi, “Deep Architecture based on DenseNet-121 Model for Weather Image Recognition,†International Journal of
Advanced Computer Science and Applications, vol. 13, no. 10, pp. 559–565, 2022, doi: 10.14569/IJACSA.2022.0131065.
[26] E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose
COVID-19 in X-Ray Images,†2020.
[27] M. M. Hasan, H. Ali, M. F. Hossain, and S. Abujar, “Preprocessing of Continuous Bengali Speech for Feature Extraction,†2020
11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, pp. 1–4, 2020,
doi: 10.1109/ICCCNT49239.2020.9225469.
[2] J. Sukoyo, E. S. Utami, and E. Kurniati, “The Development of Montessori-Based Javanese Script Learning Model,†Proceedings
of the 2nd International Conference on Innovation in Education and Pedagogy (ICIEP 2020), vol. 619, no. Iciep 2020, pp. 99–
102, 2022.
[3] J. D. Kelleher, The Deep Learning, 2019.
[4] A. Dhillon and G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object
detection,†Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85–112, 2020, doi: 10.1007/s13748-019-00203-0.
[5] N. P. Sutramiani, N. Suciati, and D. Siahaan, “Transfer Learning on Balinese Character Recognition of Lontar Manuscript Using
MobileNet,†ICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences, pp. 0–4,
2020, doi: 10.1109/ICICoS51170.2020.9299030.
[6] S. K. C and C. Nattee, “Handwritten Alphanumeric Character Recognition,†vol. 12, no. 1, pp. 20–30, 2023, doi:
10.5072/FK26H4PV9J.2023.05.08.001.
[7] V. Madane, K. Ovhal, and M. Bhong, “Handwriting Recognition Using Artificial Intelligence Neural Network and Image
Processing,†International Research Journal of Modernization in Engineering Technology and Science, no. 03, pp. 1432–1447,
2023, doi: 10.56726/irjmets34395.
[8] S. F. M¨uller-Cleve, V. Fra, L. Khacef, A. Peque˜no-Zurro, D. Klepatsch, E. Forno, D. G. Ivanovich, S. Rastogi, G. Urgese,
F. Zenke, and C. Bartolozzi, “Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic
hardware,†Frontiers in Neuroscience, vol. 16, 2022, doi: 10.3389/fnins.2022.951164.
[9] U. BS and U. K, “a Review Paper on Ocr Using Convolutional Neural Networks,†International Journal of Engineering Applied
Sciences and Technology, vol. 7, no. 7, pp. 102–106, 2022, doi: 10.33564/ijeast.2022.v07i07.018.
[10] M. F. Muhdalifah, “Pooling Comparison in CNN Architecture for Javanese Script Classification,†International Journal of
Informatics and Computation, vol. 3, no. 2, p. 15, 2022, doi: 10.35842/ijicom.v3i2.30.
[11] A. Budiman, A. Fadlil, and R. Umar, “Identification of Learning Javanese Script Handwriting Using Histogram Chain Code,â€
EDUMASPUL: Jurnal Pendidikan, vol. 7, no. 1, pp. 147–153, 2023.
[12] M. A. Rasyidi, T. Bariyah, Y. I. Riskajaya, and A. D. Septyani, “Classification of handwritten javanese script using random
forest algorithm,†Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, pp. 1308–1315, 2021, doi:
10.11591/eei.v10i3.3036.
[13] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,†Evolutionary
Intelligence, vol. 15, no. 1, pp. 1–22, 2022, doi: 10.1007/s12065-020-00540-3.
[14] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, A survey of the recent architectures of deep convolutional neural networks.
Springer Netherlands, 2020, vol. 53, no. 8.
[15] Purwono, A. Ma’arif, W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky, and Q. M. U. Haq, “Understanding of Convolutional
Neural Network (CNN): A Review,†International Journal of Robotics and Control Systems, vol. 2, no. 4, pp. 739–748,
2022, doi: 10.31763/ijrcs.v2i4.888.
[16] S. Singh and D. Schicker, “Seven Basic Expression Recognition Using ResNet-18,†pp. 1–3, 2021.
[17] D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,†2019 IEEE 10th Annual Ubiquitous
Computing, Electronics and Mobile Communication Conference, UEMCON 2019, pp. 0280–0285, 2019, doi: 10.1109/UEMCON47517.2019.8993089.
[18] D. Gupta, A. Gupta, R. Gandhi, P. Vishwavidyalaya, and S. S. Gupta, “Identification of Alzheimer’s disease from MRI image
employing a probabilistic deep learning-based approach and the VGG16,†2023.
[19] W. Gong, X. Zhang, B. Deng, and X. Xu, “Palmprint recognition based on convolutional neural network-alexnet,†Proceedings
of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, vol. 18, pp. 313–316, 2019,
doi: 10.15439/2019F248.
[20] N. S. Shadin, S. Sanjana, and N. J. Lisa, “COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(
CNN) and InceptionV3,†2021 International Conference on Information Technology, ICIT 2021 - Proceedings, vol. 3, no.
September 2012, pp. 799–804, 2021, doi: 10.1109/ICIT52682.2021.9491752.
[21] G. S. Nugraha, M. I. Darmawan, and R. Dwiyansaputra, “Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16,
Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition,†Kinetik: Game Technology, Information System, Computer
Network, Computing, Electronics, and Control, vol. 4, no. 2, 2023, doi: 10.22219/kinetik.v8i2.1667.
[22] Z. Zhong, M. Zheng, H. Mai, J. Zhao, and X. Liu, “Cancer image classification based on DenseNet model,†Journal of Physics:
Conference Series, vol. 1651, no. 1, 2020, doi: 10.1088/1742-6596/1651/1/012143.
[23] G. Huang, Z. Liu, G. Pleiss, L. Van Der Maaten, and K. Q. Weinberger, “Convolutional Networks with Dense Connectivity,â€
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 8704–8716, 2022, doi:
10.1109/TPAMI.2019.2918284.
[24] Z. Shi, M. Chen, and Z. Wu, “Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel
Attentional Feature Fusion,†Electronics (Switzerland), vol. 12, no. 13, 2023, doi: 10.3390/electronics12132991.
[25] S. A. Albelwi, “Deep Architecture based on DenseNet-121 Model for Weather Image Recognition,†International Journal of
Advanced Computer Science and Applications, vol. 13, no. 10, pp. 559–565, 2022, doi: 10.14569/IJACSA.2022.0131065.
[26] E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose
COVID-19 in X-Ray Images,†2020.
[27] M. M. Hasan, H. Ali, M. F. Hossain, and S. Abujar, “Preprocessing of Continuous Bengali Speech for Feature Extraction,†2020
11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, pp. 1–4, 2020,
doi: 10.1109/ICCCNT49239.2020.9225469.
Downloads
Published
2024-03-28
Issue
Section
Articles
How to Cite
[1]
E. D. B. Sudewo, M. K. Biddinika, and A. Fadlil, “DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character”, MATRIK, vol. 23, no. 2, pp. 453–464, Mar. 2024, doi: 10.30812/matrik.v23i2.3855.
Similar Articles
- Anthony Anggrawan, Raisul Azhar, Bambang Krismono Triwijoyo, Mayadi Mayadi, Developing Application in Anticipating DDoS Attacks on Server Computer Machines , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 20 No. 2 (2021)
- Elly Mufida, Dedi Irawan, Giatika Chrisnawati, REMOTE SITE MIKROTIK VPN DENGAN POINT TO POINT TUNNELING PROTOCOL (PPTP) STUDI KASUS PADA YAYASAN TERATAI GLOBAL JAKARTA , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 16 No. 2 (2017)
- Husain Husain, I Putu Hariyadi, Kurniadin Abd Latif, Galih Tri Aditya, Implementation of Port Knocking with Telegram Notifications to Protect Against Scanner Vulnerabilities , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Sadam Husen, Raisul Azhar, APLIKASI MANAJEMEN DAN MONITORING UNTUK KEAMANAN PADA JARINGAN HOTSPOT , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 15 No. 2 (2016)
- Wahyu Riski Aulia Putra, Agus Reza Aristiadi Nurwa, Dimas Febriyan Priambodo, Muhammad Hasbi, Infrastructure as Code for Security Automation and Network Infrastructure Monitoring , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 1 (2022)
- Michael Michael, Frenky Tanoto, Eric Wibowo, Frenky Lutan, Abdi Dharma, Pengenalan Plat Kendaraan Bermotor dengan Menggunakan Metode Template Matching dan Deep Belief Network , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 1 (2019)
- Imam Riadi, Abdul Fadlil, Muhammad Amirul Mu'min, OWASP Framework-based Network Forensics to Analyze the SQLi Attacks on Web Servers , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Ismail Husein, Rina Widyasari, Algorithm Symmetric 2-DLDA for Recognizing Handwritten Capital Letters , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 2 (2022)
- Edi Ismanto, Januar Al Amien, Vitriani Vitriani, A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 3 (2024)
- Lathifatul Mahabbati, Andy Hidayat Jatmika, Raphael Bianco Huwae, Reducing Transmission Signal Collisions on Optimized Link State Routing Protocol Using Dynamic Power Transmission , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 1 (2024)
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Imam Riadi, Abdul Fadlil, Muhammad Amirul Mu'min, OWASP Framework-based Network Forensics to Analyze the SQLi Attacks on Web Servers , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Joko Supriyanto, Abdul Fadlil, Sunardi Sunardi, Pengujian Kualitatif Aplikasi Informasi Gempa Bumi dalam Bentuk Suara untuk Tunanetra , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- Alya Masitha, Muhammad Kunta Biddinika, Herman Herman, K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
.png)
