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All Journal JOINCS (Journal of Informatics, Network, and Computer Science)
Ika Safitri Windiarti
Universitas Muhammadiyah Malaysia
Computer Science & IT

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From Scalability to Sustainability: A 20-Year Retrospective on Deep Learning and Parameter-Efficient Fine-Tuning for Text Classification: Dari Skalabilitas ke Keberlanjutan: Tinjauan 20 Tahun tentang Pembelajaran Mendalam dan Penyesuaian Parameter yang Efisien untuk Klasifikasi Teks Andry Rachmadany; Ika Safitri Windiarti
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1711

Abstract

In the area of natural language processing (NLP), especially regarding text classification, earlier methods that relied on traditional machine learning are being increasingly replaced by neural network designs like convolutional neural networks and recurrent neural networks. Additionally, the rise of transformer-based models has led to considerable improvements in performance, though this comes with higher demands for computing power and energy usage. This paper provides a look back at the development of deep learning and Parameter-Efficient Fine-Tuning (PEFT) methods for text classification from 2005 to 2025. The research explores important technological advancements, evaluates the balance between performance, scalability, and efficient computing, and points out the rising concern for sustainability in the development of artificial intelligence. The findings show a transition from strategies aimed at simply increasing scale to those that focus on more efficiency. In this setting, PEFT has become an important advancement in easing the computing load without greatly impacting performance, although it still faces challenges in flexibility and energy consciousness. These insights are anticipated to lay the groundwork for more research into creating environmentally friendly NLP technologies.
Context-Aware Transformer-Based Model for Aspect-Based Sentiment Analysis: A Systematic Literature Review: Model Berbasis Transformer yang Sadar Konteks untuk Analisis Sentimen Berbasis Aspek: Tinjauan Literatur Sistematis Moch. Fauzan; Ika Safitri Windiarti
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1712

Abstract

Aspect-Based Sentiment Analysis (ABSA) is a critical natural language processing task aimed at identifying specific aspects within text and determining the sentiment polarity toward each aspect. Transformer-based models, particularly BERT and its variants, have demonstrated significant advances in ABSA through powerful contextual representations. However, challenges in capturing target-specific context and managing inter-subtask dependencies remain. This Systematic Literature Review (SLR) identifies, evaluates, and synthesizes current research on context-aware transformer models for ABSA, with emphasis on context-aware mechanisms, multi-task learning approaches, and BERT-family models. Following the PRISMA 2020 protocol, a structured search was conducted on the Scopus database using three Boolean queries, yielding 851 initial records. After deduplication (n=70), title/abstract screening (n=554 excluded), retrieval (n=147 not retrieved), and full-text eligibility assessment (n=48 excluded), 32 studies were included for synthesis. Three primary model categories were identified: (1) BERT baselines establishing strong end-to-end ABSA performance; (2) context-aware variants employing context-guided attention (CG-BERT, QACG-BERT, LCF-ATEPC, cascade models); and (3) multi-task transformers (BERT-MTL, RoBERTa-MTL, MTL-AraBERT, SABKG, MLEGCN) handling ABSA subtasks jointly. Reported F1-scores ranged from 50–89% across SemEval-2014/2015/2016 and domain-specific datasets. ntext-aware and multi-task transformer models represent the state of the art in ABSA. Open challenges include implicit aspect handling, cross-domain generalization, model efficiency, and evaluation of large generative language models (LLMs) for fine-grained sentiment tasks.
Co-Authors Moch. Fauzan Rachmadany, Andry