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All Journal Jurnal Riset Informatika Journal of Applied Research In Computer Science and Information Systems
Yulita Ayu Wardani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA Mery Oktaviyanti Puspitaningtyas; Kartika Puspita; Yuris Alkhalifi; Yulita Ayu Wardani
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.362

Abstract

Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
Enhancing Text Classification Performance: A Comparative Study of RNN and GRU Architectures with Attention Mechanisms Yulita Ayu Wardani; Mery Oktaviyanti Puspitaningtyas; Happid Ridwan Ilmi; Onesinus Saut Parulian
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.187

Abstract

Text classification plays a crucial role in natural language processing, and enhancing its performance is an ongoing area of research. This study investigates the impact of integrating attention mechanisms into a recurrent neural network (RNN) based architectures, including RNN, LSTM, GRU, and their bidirectional variants (BiLSTM and BiGRU), for text sentiment analysis. Three attention mechanisms Multihead Attention, Self Attention, and Adaptive Attention are applied to evaluate their effectiveness in improving model accuracy. The results reveal that attention mechanisms significantly enhance performance by enabling models to focus on the most relevant parts of the input text. Among the tested configurations, the LSTM model with Multihead Attention achieved the highest accuracy of 68.34%. The findings underscore the critical role of attention mechanisms in overcoming traditional RNN limitations, such as difficulty in capturing long-term dependencies, and highlight the potential for their application in broader text classification tasks.
Co-Authors Happid Ridwan Ilmi Kartika Puspita Mery Oktaviyanti Puspitaningtyas Onesinus Saut Parulian Yuris Alkhalifi