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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Rizka Milandga Milenio
Institut Teknologi Nasional Bandung
Computer Science & IT Electrical & Electronics Engineering

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A BERT-based modular framework for automated English essay scoring via trait analysis Jasman Pardede; Rizka Milandga Milenio; Thalita Zharifa Nathania
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11235

Abstract

Automated essay scoring (AES) systems are commonly implemented using holistic scoring, which limits interpretability and prevents assessment at the writing trait level. As a result, such systems provide limited diagnostic and actionable feedback. To address this limitation, this study proposes a modular trait-based AES framework that separates structure and grammar evaluation while maintaining an integrated scoring mechanism. The proposed framework consists of two modules. The structure module evaluates the ideas, organization, and style traits using a bidirectional encoder representations from transformer-bidirectional long short-term memory (BERT-BiLSTM-Attention) architecture trained on the automated student assessment prize (ASAP) dataset. The grammar module evaluates the Conventions trait by applying a BERT-based grammatical acceptability classifier trained on the Corpus of linguistic acceptability (CoLA) dataset, followed by multinomial logistic regression to convert grammatical patterns into interpretable grammar scores. Experiments were conducted on the ASAP dataset and evaluated using the quadratic weighted Kappa (QWK) metric. The structure module achieved a QWK score of 0.7906 on the test set, while the grammar module obtained a QWK of 0.3923. The integrated holistic score reached a QWK of 0.7847. These results demonstrate that the proposed modular framework improves interpretability and scoring performance, supporting more objective and actionable essay evaluation for formative assessment in English language education.
Improving multilabel classification of hate speech and abusive language in Indonesian using MAML Jasman Pardede; Ghixandra Julyaneu Irawadi; Rizka Milandga Milenio
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27332

Abstract

This study investigates automated multi-label detection of hate speech and abusive language (HSAL) in Indonesian social media, addressing challenges of data imbalance, especially in minority labels. Two training approaches are compared: standard supervised learning and meta-learning using the model-agnostic meta-learning (MAML) algorithm. IndoBERTweet-BiGRU is adopted as the baseline model, while MAML is leveraged to enhance generalization and adaptability with limited training data. Both models are trained on a multilabel dataset with 13 HSAL categories exhibiting highly imbalanced distributions. The best supervised model achieved an F1-Micro of 84.02% and an F1-macro of 77.97%, whereas the best MAML-trained model reached 84.12% and 76.85%, respectively. Although the overall gap is small, MAML demonstrates notable improvements on minority classes such as hate speech (HS) physical, gender, and race, shown through higher F1-score and area under the receiver operating characteristic curve (AUROC) values. These results highlight its strength in low-resource classification settings. This study is limited to Indonesian language and YouTube transcript contexts, and MAML incurs higher training complexity. Cultural and linguistic nuances also present potential bias in real-world use. Despite these constraints, the proposed system offers practical benefits by enabling fine-grained HSAL classification and supporting earlier detection of harmful online content.
Co-Authors Ghixandra Julyaneu Irawadi Jasman Pardede Thalita Zharifa Nathania