Development of Lampung Script Characters Recognition Model using TensorFlow

Meizano Ardhi Muhammad, Martinus Martinus, Adhi Nurhartanto, Yessi Mulyani, Gita Paramita Djausal, Deni Achmad, Sony Ferbangkara

Abstract


In the face of cultural erosion, particularly the dwindling proficiency in deciphering Lampung characters, this research pioneers an innovative approach to cultural preservation. The Lampung character recognition model was developed using TensorFlow, a robust computer vision and machine learning framework. Convolutional Neural Networks (CNN) are integrated to enhance the image processing capabilities. The research employs the Design Science Research methodology, emphasizing problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The dataset, comprising 3900 instances, is meticulously collected and features diverse Lampung script writing. Through preprocessing and classification, the model undergoes training with an 80:10:10 split for training, validation, and test data. The architecture includes CNN layers with ReLu activation functions, and transfer learning is employed using the MobileNet V2 network model. Demonstrating commendable performance, the model achieves an accuracy spectrum of 0.652 to 0.998. The research not only underscores the viability of the TensorFlow model but also establishes a foundation for future explorations in preserving Lampung cultural heritage. This intersection of advanced machine learning and cultural preservation signifies a promising synergy, ensuring the enduring legacy of Lampung characters amid societal and technological transformations.

Keywords


Lampung script; CNN; Machine Learning Recognition; TensorFlow

Full Text:

PDF

References


P. Y. Bagaskara, M. A. Muhammad, M. Mardiana, and M. Komarudin, “Virtual Keyboard Design of Lampung Script Based on Android,” IJECS, vol. 2, no. 1, pp. 15–22, Jun. 2022, doi: 10.24042/ijecs.v2i1.11648.

Mardiana, F. A. Arizal, M. A. Muhammad, Martinus, and G. P. Djausal, “Word Per Minute (WPM) Lampung Script Keyboard,” TEPIAN, vol. 1, no. 3, pp. 79–83, Aug. 2020, doi: 10.51967/tepian.v1i3.147.

S. Ferbangkara et al., “Usability of Lampung Heritage Virtual Reality Tour,” JESR, vol. 4, no. 2, Jan. 2023, doi: 10.23960/jesr.v4i2.107.

BPS Provinsi Lampung, “Provinsi Lampung dalam Angka 2015,” BPS Provinsi Lampung, Bandar Lampung, 1102001.18, 2015.

N. Sazqiah et al., “Pengenalan Aksara Lampung Menggunakan Metode CNN (Convolutional Neural Network),” SNIP, vol. 2, no. 1, Apr. 2022, doi: 10.23960/snip.v2i1.165.

P. Bintoro and A. Harjoko, “Lampung Script Recognition Using Convolutional Neural Network,” Indonesian J. Comput. Cybern. Syst., vol. 16, no. 1, p. 23, Jan. 2022, doi: 10.22146/ijccs.70041.

Atharva Hase, Viral Jain, Parag Gujarathi, Aditi Bhor, and Swati Bhonde, “Devanagari Character Recognition Using Deep Learning,” IJARSCT, pp. 215–220, Jun. 2022, doi: 10.48175/IJARSCT-4585.

P. Nayak and S. Chandwani, “Improved Offline Optical Handwritten Character Recognition: A Comprehensive Review using Tensorflow,” International Journal of Engineering Research, vol. 10, no. 11.

TensorFlow, “TensorFlow,” TensorFlow. Accessed: May 28, 2023. [Online]. Available: https://www.tensorflow.org/

C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “CSPNet: A New Backbone that can Enhance Learning Capability of CNN,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA: IEEE, Jun. 2020, pp. 1571–1580. doi: 10.1109/CVPRW50498.2020.00203.

K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems, vol. 24, no. 3, pp. 45–77, Dec. 2007, doi: 10.2753/MIS0742-1222240302.

N. Mawadda, C. Anwar, and A. Jatmiko, “Implementation of Religious Character Values in Scout Activities at Junior High School South Lampung,” Bulletin of Science Education, vol. 4, no. 1, pp. 135–146, 2024.

S. Pattanayak, “Introduction to deep-learning concepts and TensorFlow,” in Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python, Springer, 2023, pp. 109–197.

F. T. Anggraeny, Y. V. Via, and R. Mumpuni, “Image preprocessing analysis in handwritten Javanese character recognition,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 2, pp. 860–867, 2023.

Y. Xie et al., “Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training,” Journal of Neural Engineering, vol. 20, no. 5, p. 056037, 2023.

N. Umeorah, P. Mashele, O. Agbaeze, and J. C. Mba, “Barrier options and Greeks: Modeling with neural networks,” Axioms, vol. 12, no. 4, p. 384, 2023.

Z. Xu et al., “Agile and accurate CTR prediction model training for massive-scale online advertising systems,” presented at the Proceedings of the 2021 international conference on management of data, 2021, pp. 2404–2409.

R. L. Vallejo et al., “The accuracy of genomic predictions for bacterial cold water disease resistance remains higher than the pedigree-based model one generation after model training in a commercial rainbow trout breeding population,” Aquaculture, vol. 545, p. 737164, 2021.

M. C. Frank, M. Braginsky, D. Yurovsky, and V. A. Marchman, Variability and consistency in early language learning: The Wordbank project. MIT Press, 2021.

G. Orrù, M. Monaro, C. Conversano, A. Gemignani, and G. Sartori, “Machine learning in psychometrics and psychological research,” Frontiers in psychology, vol. 10, p. 2970, 2020.




DOI: http://dx.doi.org/10.24042/ijecs.v3i2.19878

Refbacks

  • There are currently no refbacks.


Creative Commons License

International Journal of Electronics and Communications System (IJECS) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.