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Triawan, Puas
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Computer Science & IT

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Impact of NLP Algorithms on Sentiment Analysis Efficiency and Accuracy Triawan, Puas; Tahyudin, Imam; Purwadi, Purwadi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1222

Abstract

Sentiment analysis plays a crucial role in understanding user perceptions of products and services in the digital era. However, its implementation is still constrained by the need for high computational resources. This research aims to evaluate the impact of implementing transformer-based Natural Language Processing (NLP) algorithms—such as BERT, RoBERTa, and ELECTRA—on the quality and efficiency of sentiment analysis, especially in multilingual and real-time data contexts. This study uses a Systematic Literature Review (SLR) approach with the PRISMA protocol to assess the performance, challenges, and solutions offered by various NLP models. The study results show that transformer-based models consistently outperform traditional approaches; BERT and RoBERTa can achieve accuracy above 95% with F1-scores ranging from 0.92–0.95, while ELECTRA records the highest accuracy up to 98.09% with average precision and recall above 0.90 on e-commerce data. Furthermore, the transfer learning approach has been proven to reduce training time by 50–70% compared to conventional methods, without compromising analysis quality. Nevertheless, the need for large computational power remains a major obstacle. Several strategies, such as model distillation and data augmentation, have proven effective in reducing computational load while maintaining high performance. These findings confirm that transformer-based NLP technology not only improves the quality of sentiment analysis but also opens up innovation opportunities for cross-language and cross-domain applications. This research recommends optimizing models for resource-constrained languages and developing real-time systems to achieve inclusivity and efficiency in modern data processing.
A Hybrid Feature-Enriched IndoBERT Framework for Sentiment Analysis of Ride-Hailing Service Reviews in Indonesia Triawan, Puas; Tahyudin, Imam; Purwadi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1587

Abstract

This study examines sentiment classification for Indonesian ride-hailing user reviews, which often contain informal expressions, ambiguity, and strong contextual dependency. Existing studies commonly rely on either traditional machine learning or transformer-based models, while limited attention has been given to integrating heterogeneous feature representations. To address this gap, this study proposes a feature-level hybrid integration strategy combining TF-IDF and IndoBERT embeddings. This approach enables the model to capture statistical term importance and contextual semantic meaning within a unified representation. A quantitative experimental design was applied to approximately 20,000 reviews collected from Gojek, Grab, and Maxim. Sentiment labels were generated through rating-based mapping and manually validated for consistency. The dataset, which was relatively balanced across positive, neutral, and negative classes, was divided into training and testing sets using an 80:20 split. Model performance was evaluated on the test set using accuracy, precision, recall, and F1-score. The proposed hybrid model achieved the highest accuracy of 93.5%, outperforming IndoBERT (91.8%) and traditional machine learning models (78.4%–87.6%). The results show that feature-level integration improves sentiment classification performance, although neutral sentiment remains challenging due to contextual ambiguity.
Co-Authors Imam Tahyudin Purwadi Purwadi Purwadi