Implementation Convolutional Neural Network (CNN) for Bima Script Handwriting Recognition
Implementasi Convolutional Neural Network (CNN) untuk Pengenalan Tulisan Tangan Aksara Bima
DOI:
https://doi.org/10.21070/ups.4666Keywords:
Deep learning, Recognition, Handwriting, Bima Script, Convolutional Neural NetworkAbstract
Indonesia has a wide variety of languages. In the dialect of a region, there are letter symbols that represent the expression of the region's unique language. One example of a language that shows unique characteristics in its writing system is Bima, known as the Bima script. Bima script, or Mbojo script, is a writing system traditionally used in the Bima region, located in West Nusa Tenggara Province. Bima script is still not widespread among the public, so it is important to preserve it as part of the cultural heritage of the Mbojo tribe. This research aims to train a computer to recognize Bima characters using the Convolutional Neural Network method. The dataset used consists of 2640 images of Bima script handwriting with 22 classes. The results showed a reliable performance of the CNN model, with an accuracy of 97,34%, precision 97,56%, recall 97,34%, and f1-score 97,31% on the test data.
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References
Canberra, “Indonesia.” Accessed: Feb. 06, 2024. [Online]. Available: https://www.kemlu.go.id/canberra/id/read/indonesia/2186/etc-menu
M. Alfian Tuflih, Mayong, and Nensilianti, “Pelatihan Membaca dan Menulis Aksara Bima Siswa SMK Negeri 1 Kota Bima,” PENGABDI: Jurnal Hasil Pengabdian Masyarakat, vol. 3, no. 1, pp. 1–12, 2022, doi: 10.26858/pengabdi.v3i1.33051.
Munawar Sulaiman, “Aksara Mbojo.” Accessed: Oct. 30, 2023. [Online]. Available: https://warisanbudaya.kemdikbud.go.id/?newdetail&detailCatat=6561
Walikota Bima, Peraturan Walikota Kota Bima Nomor 50 Tahun 2019 tentang Penetapan Mata Pelajaran Bahasa, Sejarah, Seni Budaya Dan Ketrampilan Sebagai Mata Pelajaran Muatan Lokal Untuk Sekolah Dasar Dan Sekolah Menengah Pertama Di Kota Bima. Bima, Indonesia: https://peraturan.bpk.go.id/Details/129985/perwali-kota-bima-no-50-tahun-2019, 2019.
F. Bimantoro, A. Aranta, G. Satya Nugraha, R. Dwiyansaputra, and A. Yudo Husodo, “Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN (Handwriting Recognition of Bima Script using Texture Features and KNN),” Journal of Computer Science and Informatics Engineering, vol. 5, no. 1, pp. 60–67, 2021, doi: 10.29303/jcosine.v5i1.387.
M. I. Fidatama, “Pengenalan Pola Tulisan Tangan Aksara Bima Menggunakan Metode Ekstraksi Ciri Local Binary Pattern, Metode Reduksi Data Latih KSupport Vector Nearest Neighbour, Dan Metode Klasifikasi K-Nearest Neighbour,” Thesis, Universitas Mataram, Mataram, 2021.
R. Aryanto and M. Alfan Rosid, “Penerapan Deep Learning untuk Pengenalan Tulisan Tangan Bahasa Akasara Lota Ende dengan Menggunakan Metode Convolutional Neural Networks (CNN),” Jurnal Informasi dan Teknologi, vol. 5, no. 4, pp. 258–264, 2023, doi: 10.37034/jidt.v5i1.313.
G. A. Noor, D. I. Mulyana, and F. Akbar, “Optimasi Image Classification Pada Burung Kenari Dengan Menggunakan Data Augmentasi dan Convolutional Neural Network,” Smart Comp : Jurnalnya Orang Pintar Komputer, vol. 11, no. 2, pp. 226–238, 2022, doi: 10.30591/smartcomp.v11i2.3530.
D. Darmawan, “Implementasi Metode Convolutional Neural Network (CNN) Dalam Mendeteksi Jenis Sampah,” Thesis, Universitas Jambi, Jambi, 2023.
A. M. T. Andar, N. Fadillah, and Munawir, “Pengenalan Tulisan Tangan Karakter Aksara Batak Toba dengan Metode Convolutional Neural Network (CNN),” Jurnal Edukasi dan Penelitian Informatika, vol. 9, no. 2, pp. 242–252, 2023, doi: 10.26418/jp.v9i2.64242.
A. Maharil, “Perbandingan Arsitektur VGG16 Dan ResNet50 Untuk Rekognisi Tulisan Tangan Aksara Lampung,” Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), vol. 3, no. 2, pp. 236–243, 2022, doi: 10.33365/jatika.v3i2.2030.
A. Willyanto, D. Alamsyah, and H. Irsyad, “Identifikasi Tulisan Tangan Aksara Jepang Hiragana Menggunakan Metode CNN Arsitektur VGG-16,” Jurnal Algoritme, vol. 2, no. 1, pp. 1–11, 2021, doi: 10.35957/algoritme.v2i1.1450.
T. Q. Vinh, L. H. Duy, and N. T. Nhan, “Vietnamese handwritten character recognition using convolutional neural network,” IAES International Journal of Artificial Intelligence, vol. 9, no. 2, pp. 276–283, Jun. 2020, doi: 10.11591/ijai.v9.i2.pp276-283.
I. Khandokar, M. Hasan, F. Ernawan, S. Islam, and M. N. Kabir, “Handwritten character recognition using convolutional neural network,” J Phys Conf Ser, vol. 1918, no. 4, pp. 1–5, Jun. 2021, doi: 10.1088/1742-6596/1918/4/042152.
M. Junihardi, S. Sanjaya, L. Handayani, and F. Syafria, “Klasifikasi Daging Sapi Dan Daging Babi Menggunakan Arsitektur EfficientNet-B3 Dan Augmentasi Data,” Jurnal TEKINKOM, vol. 6, no. 1, pp. 16–25, 2023, doi: 10.37600/tekinkom.v6i1.845.
M. D. Nadarajan, S. Raghava, S. Giri, and B. Kumar Depuru, “Enhancing Warehouse Operations Through Artificial Intelligence: Pallet Damage Classification with Deep Learning Insights,” Int J Innov Sci Res Technol, vol. 8, no. 11, pp. 2556–2563, 2023, doi: 10.5281/zenodo.10391024.
R. Adam, “Image Classification Dengan Cnn Dan Tensorflow.” Accessed: Feb. 08, 2024. [Online]. Available: https://structilmy.com/blog/2021/01/18/image-classification-dengan-cnn-dan-tensorflow/
A. A. Asiri et al., “Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images,” Computers, Materials and Continua, vol. 73, no. 3, pp. 5735–5753, 2022, doi: 10.32604/cmc.2022.031747.
O. A. Shawky, A. Hagag, E. S. A. El-Dahshan, and M. A. Ismail, “Remote sensing image scene classification using CNN-MLP with data augmentation,” Optik (Stuttg), vol. 221, Nov. 2020, doi: 10.1016/j.ijleo.2020.165356.
A. M. Saleh and T. Hamoud, “Analysis And Best Parameters Selection For Person Recognition Based On Gait Model Using CNN Algorithm And Image Augmentation,” J Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-020-00387-6.
Y. Tian, “Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm,” IEEE Access, vol. 8, pp. 125731–125744, 2020, doi: 10.1109/ACCESS.2020.3006097.
A. Peryanto, A. Yudhana, and D. R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” Format : Jurnal Ilmiah Teknik Informatika, vol. 8, no. 2, pp. 138–147, 2019, doi: 10.22441/format.2019.v8.i2.007.
S. Dahiya, T. Gulati, and D. Gupta, “Performance Analysis Of Deep Learning Architectures For Plant Leaves Disease Detection,” Measurement: Sensors, vol. 24, Dec. 2022, doi: 10.1016/j.measen.2022.100581.
D. Cindy Agustin, M. Alfan Rosid, and N. Ariyanti, “Implementasi Convolutional Neural Network Untuk Deteksi Kesegaran Pada Apel,” Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer), vol. 13, no. 2, pp. 145–150, 2023, doi: 10.37859/jf.v13i02.5175.
A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, Jan. 2022, doi: 10.1016/j.aiia.2021.12.002.
M. Rafly Alwanda, R. Putra, K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.
K. O. Afebu, Y. Liu, and E. Papatheou, “Feature-based intelligent models for optimisation of percussive drilling,” Neural Networks, vol. 148, pp. 266–284, Apr. 2022, doi: 10.1016/j.neunet.2022.01.021.
M. Yildirim and A. Cinar, “Classification of Alzheimer’s disease MRI images with CNN based hybrid method,” Ingenierie des Systemes d’Information, vol. 25, no. 4, pp. 413–418, Aug. 2020, doi: 10.18280/isi.250402.
D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion matrix,” Comput Oper Res, vol. 152, Apr. 2023, doi: 10.1016/j.cor.2022.106131.
KantinIT, “Confusion Matrix: Pengertian, Cara Kerja dan Contoh Soal.” Accessed: Feb. 17, 2024. [Online]. Available: https://kantinit.com/kecerdasan-buatan/confusion-matrix-pengertian-cara-kerja-dan-contoh-soal/
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