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All Journal Jurnal Sains Dan Teknologi (SAINTEKBU) INOVTEK Polbeng - Seri Informatika KOMPUTIKA - Jurnal Sistem Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal La Multiapp Curtina
Arifia, Amaludin
Program Studi Teknik Informatika, Universitas PGRI Ronggolawe Tuban, Jawa Timur
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Journal La Multiapp

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Food and Beverage Product Review Sentiment Analysis on E-Commerce with Word Embedding and LSTM Bowo, Herry; Suryanto, Andik Adi; Arifia, Amaludin
Journal La Multiapp Vol. 6 No. 5 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i5.2468

Abstract

Sentiment analysis is a widely used method to understand customer opinions about a product. This study aims to analyze the sentiment of food and beverage product reviews on the Tokopedia marketplace using the Long Short-Term Memory (LSTM) approach and word embedding. The data used consisted of customer reviews that were categorized into three sentiment classes, namely positive, neutral, and negative. The model was developed through a series of stages of preprocessing, embedding, training with LSTM, as well as performance evaluation using accuracy and F1-score metrics. The results show that the developed model is able to classify sentiment with a fairly high level of accuracy. Based on the results of the final test on 5,000 data, the model managed to classify 122 data as negative, 130 data as neutral, and 4,871 data as positive, although it still showed an imbalance in class classification. Further analysis through word cloud visualization showed that words like "delicious", "steady", and "good" dominated the positive sentiment, while words like "disappointed", "broken", and "slow" often appeared in negative sentiment. This study provides valuable insights for businesses in understanding customer opinions and improving the quality of products and services.
Co-Authors Adi Suryanto, Andik Afifuddiin, Mohammad Agustia Hidayatullah, Zaebi Alfian Nurlifa Andik Adi Suryanto Aries Eko Prasetyo Aris Wijayanti Asfan Muqtadir Bowo, Herry Dwi Kurnia Basuki Farkhan, Muhamad Farir Fitroh Amaluddin Fitroh Amaluddin Ghozali, Daniel Reredo Ahmad Haryoko, Andy Heru Prastyo Khoirianti, Rini Mochamad Zaenal Fanani Ningsih, Ulfia Ratna Putra, Angga Dwiga Mahendra Putri, Shinta Widayati Ridiani, Nia Maulina ROCHMAH, AINUR Rosyida, Arina Sari, Fitria Atika Sasmita, Niken Diah Syamsul Arifin Ustadatin, Fina