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All Journal Creative Information Technology Journal SISFOTENIKA CogITo Smart Journal Jurnal ICT : Information Communication & Technology Technologia: Jurnal Ilmiah E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Manajemen Informatika dan Sistem Informasi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia EXPLORE Journal of Informatics, Electrical and Electronics Engineering Jurnal Informatika Polinema (JIP)
Asro Nasiri
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Journal of Informatics, Electrical and Electronics Engineering

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Efektivitas Pelatihan Awal Berbasis Domain Spesifik Legal-BERT Untuk Natural Language Processing Hukum: Replikasi Dan Perluasan Studi Casehold Zakiri, Hasani; Alva Hendi Muhammad; Asro Nasiri
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2610

Abstract

Abstract?The emergence of domain-specific language models has demonstrated significant potential across various specialized fields. However, their effectiveness in legal natural language processing (NLP) remains underexplored, particularly given the unique challenges posed by legal text complexity and specialized terminology. Legal NLP has practical applications such as automated legal precedent search and court decision analysis that can accelerate legal research from weeks to hours. This study evaluates the CaseHOLD dataset to provide comprehensive empirical validation of domain-specific pretraining benefits for legal NLP tasks with focus on data efficiency and context complexity analysis. We conducted systematic experiments using the CaseHOLD dataset containing 53,000 legal multiple-choice questions. We compared four models: BiLSTM, BERT-base, Legal-BERT, and RoBERTa across varying data volumes (1%, 10%, 50%, 100%) and context complexity levels. Paired t-tests with 10-fold cross-validation and Bonferroni correction ensure robust methodology that guarantees finding reliability. Legal-BERT achieved the highest macro-F1 score of 69.5% (95% CI: [68.0, 71.0]), demonstrating a statistically significant improvement of 7.2 percentage points over BERT-base (62.3%, p < 0.001, Cohen's d= 1.23). RoBERTa showed competitive performance at 68.9%, nearly matching Legal-BERT. The most substantial improvements occurred under limited data conditions with 16.6% improvement at 1% training data. Context complexity analysis revealed an inverted-U pattern with optimal performance on 41-60 word texts. The introduced Domain Specificity Score (DS-score) showed strong positive correlation (r = 0.73, p < 0.001) with pretraining effectiveness, explaining 53.3% of performance improvement variance. These findings provide empirical evidence that domain-specific pretraining offers significant advantages for legal NLP tasks, particularly under data-constrained conditions and moderate-high context complexity. The key distinction of this research is the development of a predictive DS-score framework enabling benefit estimation before implementation, unlike previous studies that only evaluated post-hoc performance. The results have practical implications for developing legal NLP systems in resource-limited environments and provide optimal implementation guidance for Legal-BERT.
Co-Authors Acihmah Sidauruk Alva Hendi Muhamad Alva Hendi Muhammad Amrullah Andi Oktafiqurahman Andi Sunyoto Arga Fiananta Arief Setyanto Arif Laksito Bambang Soedijono WA Eka Wahyu Pujiharto Eka Wahyu Sholeha Elvis Pawan Ema Utami Kusrini Kusrini Kusrini Kusrini Kusrini _ Maykel Sonobe Mohamad Syafri Lamato Muhamad Rodi Prasetya, Rendra Putranto, Dinar Wakhid Rizfi Syarif Tohir Ismail Zakiri, Hasani