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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Communication in Biomathematical Sciences JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Jurnal INFOTEL Cahaya Pengabdian LOGIC: Jurnal Penelitian Informatika
Aditya Firman Ihsan
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Sulistiyo, Mahmud Dwi; Putrada, Aji Gautama; Ihsan, Aditya Firman; Yunanto, Prasti Eko; Richasdy, Donny; Sailellah, Hassan Rizky Putra; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Co-Authors Aji Gautama Putrada Andi Fadil Adiyaksa Anugrah, M. Taufik Conny Tria Shafira Deni Saepudin Didit Adytia Djatnika Widia Nugraha Donni Richasdy Donny Richasdy Hery Widijanto Indidharmanto, Ahmad Maulana M. Taufik Anugrah Made Mita Wikantari Mahadzir, Shuhaimi Mahmud Dwi Sulistiyo Muhammad Daffa Regenta Sutrisno Muhammad Helmy Faishal Muzakky, Faqih Ahmad Prasti Eko Yunanto Pratama, Alif Adwitiya Puteri, Nurhidayah Putra, Rachmat Dwi Putu Harry Gunawan Raisa Benaya Rheza Ramadhan Putra Richasdy, Donny Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Sri Suryani Prasetiyowati Sujadi, Cika Carissa Sulistyo, Mahmud Dwi Uruqul Nadhif Dzakiy Widi Astuti Yuliant Sibaroni Yusuf, Afrizal Syahruluddin