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All Journal JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Informatika Polinema (JIP) Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert
Cut Agusniar
Universitas Malikussaleh
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Sentiment Analysis of E-Commerce Product Reviews on Tokopedia Using Support Vector Machine Alaiya, Azna; Nurdin, Nurdin; Agusniar, Cut
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10977

Abstract

This research aims to analyze the performance of Support Vector Machine (SVM) algorithm in classifying sentiment of e-commerce product reviews on the Tokopedia platform using web scraping data of 571 reviews from the 2024 period. The data includes review text variables, publication dates, and usernames processed through text preprocessing (text cleaning, stopword removal, stemming with Sastrawi), auto-labeling using a lexicon-based approach, and TF-IDF feature extraction with optimal parameters (max_features=5000, ngram_range=(1,2)) resulting in 1,187 features. Data splitting was performed using stratified method with proportions of training (80%) and testing (20%) on 461 reviews from binary classification filtering (positive vs negative). The research results demonstrate that Support Vector Machine with linear kernel achieved excellent performance with accuracy 95.70%, precision 95.89%, recall 95.70%, and F1-score 94.89% on the testing set. Despite the imbalanced dataset characteristics (92.4% positive vs 7.6% negative), SVM effectively handled the classification task by identifying negative sentiment with 100% precision and 42.86% recall, demonstrating its robustness in handling skewed data distribution. TF-IDF feature analysis identified the highest discriminative words such as "suitable", "goods", and "good" that are relevant for classifying consumer sentiment towards e-commerce products. The results indicate that SVM algorithm is highly effective for sentiment classification of e-commerce product reviews, making it suitable for practical implementation in automated sentiment analysis systems for online marketplaces.
Co-Authors Achriadi Sukiman, T Sukma Alaiya, Azna Aryanti, Leni Asran Asran b, bustami Dahlan Abdullah Dalimunthe, Rodiatul Adawiyah Della Adelia, Della Eva Darnila Ezwarsyah Ezwarsyah Fazira, Ira Fidyatun Nisa Fitri, Anisa Amelia Gemasih, Husna Ginting, Andriyan Herfia Rhomadhona Khaira, Raziatul laili wahyunita, laili Maryana Mochamad Ari Saptari Muhammad Rizki Mulya Ulfa, Septia Munauwarah, Munauwarah Munirul Ula Muslimah Siregar, Khanifa Mutammimul Ula Muthmainnah Muthmainnah Nisa Ul Fadila Nurdin Nurdin Nurdin Nurdin Qasos, Lailil Rahma Fitria, Rahma Ramadan, Rifki Rasmiati Rasyid Rini Meiyanti Rizal S.Si., M.IT, Rizal Safwandi Safwandi Sahputra, Ilham Sinambela, Ilmi Suciani Sujacka Retno Syibral Malasyi Syukriah Syukriah, Syukriah Ulfa, Nur Saufani Veri Ilhadi Yurni, Irma Zubaedah, Reza Zuraida