Sentiment Analysis of Pre-Loved Shoe Product Sales Based on X Reviews with a Comparison of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Algorithms

Setyo Harry Nugroho; Al-khowarizmi

doi:10.59934/jaiea.v5i3.1773

Authors

Setyo Harry Nugroho Universitas Muhammadiyah Sumatera Utara
Al-khowarizmi Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i3.1773

Keywords:

Sentiment Analysis, Preloved Shoes, SVM, LSTM, X (Twitter).

Abstract

The rapid growth of social media enables consumers to express opinions about products openly, including preloved shoes. These reviews are crucial as they can influence purchase intentions and brand perception. This study aims to analyze user reviews on the X (Twitter) platform using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. A total of 1,005 reviews were collected, then preprocessed and balanced into 738 data consisting of positive and negative sentiments. The results show that SVM achieved an accuracy of 68%, while LSTM obtained 61.49% in its best configuration. Thus, SVM demonstrates better efficiency in classifying simple text, whereas LSTM requires more complex parameters to achieve optimal performance. This research is expected to serve as a reference for utilizing sentiment analysis to support business decision-making in the preloved product market.

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References

S. Akmala, “Perkembangan Internet Pada Generasi Muda Di Indonesia Dengan Kaitan Undang-Undang Ite Yang Berlaku,” Cyber Secur. dan Forensik Digit., vol. 1, no. 2, pp. 45–49, 2019, doi: 10.14421/csecurity.2018.1.2.1371.

S. Wulandari and F. N. Hasan, “Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 768, 2024, doi: 10.30865/mib.v8i2.7520.

G. GROSSECK and C. HOLOTESCU, “Can we use Twitter in educational activities ?,” 4Th Int. Sci. Conf. …, no. October, 2015, [Online]. Available: http://www.researchgate.net/profile/Gabriela_Grosseck/publication/228685205_Can_we_use_Twitter_for_educational_activities/links/0fcfd50aa46c8394e6000000.pdf

PARDEDE, A., FAUZI, A., MAULITA, Y., MARPAUNG, P., & ARIANTO, S. (2025). NON-LINEAR SMART MODEL SUPPORT VECTOR MACHINE (SVM) AS A HOAX NEWS CLASSIFIER. Journal of Theoretical and Applied Information Technology, 103(22).

Suhada, S., & Pardede, A. M. H. (2019). PENGKLASTERAN DOKUMEN DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR CLUSTERING. Jurnal Sistem Informasi Kaputama (JSIK), 3(2), 1-6.

Nouval, M., Habibi, F. M., Rahmi, A., Amru Bittaqwa, M. D., & Agustianto, R. (2026). Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 5(2), 2223–2228. https://doi.org/10.59934/jaiea.v5i2.1830

Setyawan, J. (2026). Sentiment Analysis of Honda Esaf Frame Quality Based on Reviews on Platform X using Support Vector Machine Algorithm. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 5(2), 2477–2483. https://doi.org/10.59934/jaiea.v5i2.1901Darusman, A. (2026). Sentiment Analysis of Mie Gacoan Pemuda Cirebon Restaurant Reviews Using Support Vector Machine. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 5(2), 2490–2494. https://doi.org/10.59934/jaiea.v5i2.1905