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All Journal Sistemasi: Jurnal Sistem Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Management of Information System Journal Jurnal Karya untuk Masyarakat (JKuM) Journal of Artificial Intelligence and Legal Technology
Egi Dio Bagus Sudewo
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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FastViT-Based Lightweight Batak Script Recognition for Cultural Digital Preservation Egi Dio Bagus Sudewo; Hainur Rasyid; Muhammad Kunta Biddinika; Kariyamin; Dini Khairunnisa Tanjung
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 5 No. 3 (2026): Agustus
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v5i3.8850

Abstract

Batak script is a cultural heritage of the Batak people in North Sumatra that requires preservation through digitization. This study proposes the FastViT-SA12 architecture for handwritten Batak script recognition using the Batak Char 20 dataset, which consists of 117 character classes. To address data imbalance, data augmentation was performed using ImgAug, resulting in 650 images per class. The dataset was divided into training, validation, and testing sets with a ratio of 70:20:10, and all images were resized to 64 × 64 pixels to reduce computational overhead. The model was trained for 30 epochs using the Adam optimizer with a learning rate of 0.001 and a batch size of 64. The proposed model achieved a training accuracy of 99.97%, a validation accuracy of 97.96%, and a test accuracy of 89.31%, with weighted precision, recall, and F1-score of 91.84%, 89.31%, and 88.99%, respectively. FastViT-SA12 demonstrates promising potential for recognizing 117 Batak script classes while maintaining computational efficiency with a compact parameter footprint, making it a viable candidate for mobile-first Optical Character Recognition (OCR) systems in manuscript preservation.
Co-Authors Abdul Fadlil Abdul Fadlil Abdul Fadlil Al Hafiz, Afdal Andri Syahfikri Azyana Alda Sirait Bela Astuti Dini Khairunnisa Tanjung Fadila Khairani Hainur Rasyid Ibnutama, Khairi Irawati Julpan Hartono Suria Manja Manurung Jumiah Kariyamin Kariyamin, Kariyamin Khoirul Anam Dahlan Kintung Prayitno, Kintung Mhd Gilang Suryanata Mufaddal Al Baqir Mufaddal Al Baqir Muhammad Kunta Biddinika Muhammad Kunta Biddinika Murinto - Murinto - Niken Ayu Nur Elby Hajjiah Putri Nuri Ummahatina Reni Yunita Virdiana Sriviana Fatmawaty Virdiana Sriviana Fatmawaty