DOI of the published article https://doi.org/10.57152/malcom.v5i2.1814
Implementation of Convolutional Neural Networks Algorithm for Javanese Handwriting Recognition
Penerapan Algoritma Convolutional Neural Networks untuk Pengenalan Tulisan Tangan Aksara Jawa
DOI:
https://doi.org/10.21070/ups.7698Keywords:
Accuracy, onvolutional Neural Network, Deep Learning, andwritten Recognition, Javanese ScriptAbstract
Javanese script is a traditional writing system that was once widely used in East Java and Central Java, consisting of 20
main characters along with several additional attributes. However, its usage in daily life has significantly declined over
time. This study aims to develop a Javanese script recognition system using a Convolutional Neural Network (CNN) as an
effort to preserve it. The dataset utilized consists of 1,000 images of handwritten Javanese script, with 700 images allocated
for training and 300 images for validation. The research process includes data collection, preprocessing, CNN architecture
development, and model evaluation. The CNN architecture is designed to capture the key features of the script, including
distinguishing visually similar characters. Evaluation results indicate strong performance, achieving 99,83% accuracy in
recognizing input Javanese script, with consistent accuracy and loss graphs between training and validation data.
Downloads
References
S. D. Hartiyani, A. Prayogo, and E. Erlinawati, “Aplikasi Multimedia Pembelajaran Aksara Jawa Berbasis Android Untuk Siswa Sekolah Dasar,” EDUSAINTEK J. Pendidikan, Sains dan Teknol., vol. 10, no. 2, pp. 679–693, 2023, doi: 10.47668/edusaintek.v10i2.805.
D. Fakhruddin, A. Sachari, and N. Haswanto, “Pengembangan Desain Informasi dan Pembelajaran Aksara Jawa melalui Media Website,” ANDHARUPA J. Desain Komun. Vis. Multimed., vol. 5, no. 01, pp. 1–23, 2019, doi: 10.33633/andharupa.v5i01.1990.
M. Muslih and E. H. Rachmawanto, “Convolutional Neural Network (Cnn) Untuk Klasifikasi Citra Penyakit Diabetes Retinopathy,” SKANIKA, vol. 5, no. 2, pp. 167–176, 2022, doi: 10.36080/skanika.v5i2.2945.
K. Azmi, S. Defit, and S. Sumijan, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat,” J. Unitek, vol. 16, no. 1, pp. 28–40, 2023, doi: 10.52072/unitek.v16i1.504.
R. Kusumaningrum, I. Z. Nisa, R. P. Nawangsari, and A. Wibowo, “Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning,” Int. J. Adv. Intell. Informatics, vol. 7, no. 3, pp. 292–303, 2021, doi: 10.26555/ijain.v7i3.737.
A. Mulyanto, E. Susanti, F. Rossi, W. Wajiran, and R. I. Borman, “Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR),” J. Edukasi dan Penelit. Inform., vol. 7, no. 1, p. 52, 2021, doi: 10.26418/jp.v7i1.44133.
R. Aryanto, M. Alfan Rosid, and S. Busono, “Penerapan Deep Learning untuk Pengenalan Tulisan Tangan Bahasa Aksara Lota Ende dengan Menggunakan Metode Convolutional Neural Networks,” J. Inf. dan Teknol., vol. 5, no. 1, pp. 258–264, 2023, doi: 10.37034/jidt.v5i1.313.
E. D. B. Sudewo, M. K. Biddinika, and A. Fadlil, “Javanese Script Hanacaraka Character Prediction With Resnet-18 Architecture,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 10, no. 2, pp. 363– 370, 2024, doi: 10.33330/jurteksi.v10i2.3017.
M. A. S. M. Afendi and M. Yusoff, “A sound event detection based on hybrid convolution neural network and random forest,” IAES Int. J. Artif. Intell., vol. 11, no. 1, pp. 121–128, 2022, doi: 10.11591/ijai.v11.i1.pp121-128.
A. Kirana, H. Hikmayanti, and J. Indra, “Pengenalan Pola Aksara Sunda dengan Metode Convolutional Neural Network,” Sci. Student J. Information, Technol. Sci., vol. 1, no. 2, pp. 95–100, 2020, [Online]. Available: http://journal.ubpkarawang.ac.id/mahasiswa/index.php/ssj/article/download/19/15
D. S. Wita and D. Y. Liliana, “Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network (CNN),” J. Rekayasa Teknol. Inf., vol. 6, no. 1, p. 1, 2022, doi: 10.30872/jurti.v6i1.7100.
E. H. Rachmawanto and P. N. Andono, “Deteksi Karakter Hiragana Menggunakan Metode Convolutional Neural Network,” J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 183–191, 2022, doi: 10.23887/janapati.v11i3.50144.
W. Setiawan, “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus,” J. Simantec, vol. 7, no. 2, pp. 48–53, 2020, doi: 10.21107/simantec.v7i2.6551.
I. Gusti, A. Gede, and A. Kadyanan, “Pengembangan Aplikasi Deep Learning untuk Identifikasi Kain Endek Bali,” J. Ilmu Komput., vol. 14, no. 1, pp. 32–39, 2022.
I. Akil and I. Chaidir, “Deteksi Karakter Huruf Arab Dengan Menggunakan Convolutional Neural Network,” INTI Nusa Mandiri, vol. 15, no. 2, pp. 183–188, 2021, doi: 10.33480/inti.v15i2.2179.
R. N. S. Amriza and D. Supriyadi, “Komparasi Metode Machine Learning dan Deep Learning untuk Deteksi Emosi pada Text di Sosial Media,” J. Penelit. Ilmu dan Tek. Komput., vol. 13, no. 2, pp. 130–139, 2021.
I. Arifin, R. F. Haidi, and M. Dzalhaqi, “Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab,” J. Teknol. Terpadu, vol. 7, no. 2, pp. 98–107, 2021, doi: 10.54914/jtt.v7i2.436.
Efanntyo and A. R. Mitra, “Perancangan Aplikasi Sistem Pengenalan Wajah Dengan Metode Convolutional Neural Network ( CNN ) Untuk Pencatatan Kehadiran Karyawan,” J. Instrumentasi dan Teknol. Inform., vol. 3, no. 1, pp. 1–11, 2021.
N. P. Wulandari and D. Fitrianah, “Analisa Perbandingan Algoritma CNN Dan MLP Dalam Mendeteksi Penyakit COVID-19 Pada Citra X-Ray Paru,” J. Sains, Apl. Komputasu dan Teknol. Inf., vol. 3, no. 2, p. 44, 2021.
D. J. P. Manajang, S. R. U. . Sompie, and A. Jacobus, “Implementasi Framework Tensorflow Object Detection Dalam Mengklasifikasi Jenis Kendaraan Bermotor,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 4, pp. 1821–1831, 2021, doi: 10.35957/jatisi.v8i4.1269.
M. Ihsan, R. K. Niswatin, and D. Swanjaya, “Deteksi Ekspresi Wajah Menggunakan Tensorflow,” Joutica, vol. 6, no. 1, p. 428, 2021, doi: 10.30736/jti.v6i1.554.
N. F. Hasan, K. Kusrini, and H. Al Fatta, “Analisis Arsitektur Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Air Minum Dalam Kemasan,” J. Rekayasa Teknol. Inf., vol. 3, no. 1, p. 1, 2019, doi: 10.30872/jurti.v3i1.2290.
S. Prihatiningsih, N. Shafiy M, F. Andriani, and N. Nugraha, “Analisa Performa Pengenalan Tulisan Tangan Angka Berdasarkan Jumlah Iterasi Menggunakan Metode Convolutional Neural Network,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 1, pp. 58–66, 2019, doi: 10.35760/tr.2019.v24i1.1934.
Downloads
Additional Files
Posted
License
Copyright (c) 2025 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
