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All Journal International Journal of Advances in Intelligent Informatics PROtek : Jurnal Ilmiah Teknik Elektro Jurnal INSYPRO (Information System and Processing) JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah Vertex Elektro
Ridwang Ridwang, Ridwang
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Smart Verification of High School Student Reports Using Optical Character Recognition and BERT Models Syahyadi, Asep Indra; Afif, Nur; Yusuf, Ahmad; Setiaji, Haris; Ridwang, Ridwang; Irfan, Mohammad
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2764.252-261

Abstract

This study proposes an intelligent framework for verifying high school report cards with diverse layouts by integrating Optical Character Recognition (OCR) and a fine-tuned BERT model. While previous works primarily address document formats with uniform structures, this research specifically tackles the heterogeneity of report cards that differ in subject arrangement, naming conventions, and grade presentation across schools. The system was trained and evaluated using 1,000 Indonesian high school report card pages encompassing 20 subjects, both core (e.g., Mathematics, Indonesian History, Religious Education) and non-core (e.g., Arts and Culture, Physical Education). OCR was employed to extract textual content from scanned or image-based report cards, while BERT handled contextual mapping between subjects and corresponding grades. The dataset was divided into 80% for training and 20% for validation, and the model was fine-tuned on the IndoBERT-base architecture. Experimental results showed that the proposed OCR–BERT pipeline achieved an average accuracy of 97.7%, with per-subject accuracies ranging from 96% to 99%. The model exhibited high robustness in handling inconsistent layouts and minimizing deviations between actual and detected grades. Comparative analysis indicated that this hybrid approach outperforms traditional OCR-only or CNN-based methods, which are typically constrained by fixed template assumptions and lack contextual understanding. The proposed system demonstrates practical relevance for large-scale admission platforms such as SPAN-PTKIN, where manual verification of thousands of report cards is laborious and error-prone. By automating the verification process, the framework reduces human workload, enhances accuracy, and supports fairer, data-driven admission decisions. Future research will explore multimodal integration of textual and visual features, expansion to broader datasets, and application to other academic documents such as transcripts and diplomas. Overall, this work contributes a scalable, accurate, and context-aware solution for educational data verification in heterogeneous document environments.
Co-Authors Abdul Hafid Abdullah, Suhardin bin ADRIANI ADRIANI AFIF, NUR Ahmad Yusuf Ardiansah, Ahmad Asep Indra Syahyadi Atika, Cindy Nur Bakti, Rizki Yusliana DARMATASIA, DARMATASIA Duyo, Rizal Ahdiyat erlangga, Febri Farhan, Muh Hasanuddin, Muhammad Hasrul Hidayat, Muh Sultan Agung Ilham, Nur Indra Syahyadi, Asep Islam, Fajrul JUSRIL, JUSRIL Katu, Umar Khadijah, St Lateko, Andi Abd Halik LUKMAN ANAS Mohammad Irfan Muhammad Abrar Muhammad Irfandi, Muhammad Muhyiddin A.M Hayat Muniardi, Muniardi Muslimin Putra, Dilan Adrian Nasri, A Nasruddin Nasruddin Nirwana, Hafsah Nur Afif Nur Rahmah Nuraeni, Mira Paradita, Yuyun R, Wahyudi Pratama Rahmania Rahmania Rahmaniah, Rahmaniah Reski, Muhammad Rifaat, Andi Bajiel Sahrim, Mus’ab Sandi, Kurnia Sephiani, Fany Setiaji, Haris SYAFAR, A. MUHAMMAD SYAHYADI, ASEP INDRA Titin Wahyuni Utami, Anisa NurLatifa wahyu, anjas ade Wahyuddin Saputra Wawandi, Muh Arsyil