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Jurnal Informatika Universitas Pamulang
Published by Universitas Pamulang
ISSN : 25411004     EISSN : 26224615     DOI : https://doi.org/10.32493
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Universitas Pamulang is a periodical scientific journal that contains research results in the field of computer science from all aspects of theory, practice and application. Papers can be in the form of technical papers or surveys of recent developments research (state-of-the-art). Topics cover the following areas (but are not limited to): Artificial Intelligence Big Data Business Intelligence Data mining Decision Support Systems Intelligent Systems Machine Learning Network and Computer Security Optimization Pattern Recognition Soft Computing Software Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 631 Documents
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Gated Recurrent Unit with Self-Attention for Sentiment Analysis of Amazon Kindle Store Reviews Susanto, Nanang
Jurnal Informatika Universitas Pamulang Vol 10 No 4 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i4.54830

Abstract

Sentiment analysis of customer reviews is vital for e-commerce, yet conventional CNNs and LSTMs face architectural constraints when processing unstructured Amazon Kindle Store feedback data. Specifically, CNNs prioritize local n-gram features over long-range semantic dependencies, while LSTMs often suffer from information dilution in the lengthy narratives typical of e-book product reviews. To address these identified research gaps and technical challenges, this study proposes an enhanced hybrid deep learning architecture integrating Gated Recurrent Units (GRU) with a Self-Attention mechanism. The methodology utilizes a large-scale dataset of 982,619 review instances, mapping five-point rating scales into binary sentiment categories while employing trainable GloVe embeddings and fixed-length sequences of 100 tokens to capture intricate domain-specific features. Furthermore, Random Oversampling is rigorously applied to mitigate inherent class imbalances between positive and negative reviews. Experimental results demonstrate that the GRU-Attention architecture achieves a superior classification accuracy of 0.973 and a training loss of 0.0930, significantly outperforming the CNN (0.961) and LSTM (0.948) baselines. The proposed model effectively prioritizes sentiment-critical tokens within reviews, attaining balanced Precision, Recall, and F1-scores of 0.973. These findings confirm the efficacy of attention-based recurrent networks in modeling unstructured textual data, offering stakeholders high-precision analytical insights to optimize customer satisfaction strategies and objectives.

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