Evaluation of Ratings and Review Sentiments Using SVM and Random Forest for Detecting Product Authenticity on Tokopediax

Rafael Haryo Abhisena, Y.B. Dwi Setianto

Abstract


The digital era has made online shopping easier, but it has also presented significant challenges regarding product authenticity, especially for branded goods that are often counterfeited. This study aims to detect discrepancies between product ratings and reviews for Adidas products on Tokopedia. A total of 2,180 data points were obtained using web scraping techniques with Python. The data was processed through data pre-processing and feature extraction using TF-IDF. Machine learning algorithms Support Vector Machine (SVM) and Random Forest were applied with a training and testing data split of 80:20. The final evaluation results showed that the SVM model achieved an accuracy of 93.1% and an F1 score of 92.5%, while Random Forest achieved an accuracy of 83.7% and an F1 score of 82.7%. This indicates that sentiment analysis can be effectively used to detect discrepancies between ratings and reviews, which can support the development of product authenticity detection systems on e-commerce platforms.

Keywords


e-commerce; product authenticity; random Forest; sentiment analysis; support Vector Machine; tokopedia

Full Text:

PDF

References


W. F. Mustafa, S. Hidayat, and D. H. Fudholi, “Prediksi Retensi Pengguna Baru Shopee Menggunakan Machine Learning,” J. MEDIA Inform. BUDIDARMA, vol. 8, no. 1, p. 612, Jan. 2024, doi: 10.30865/mib.v8i1.7074.

H. Utami, “Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network,” Indones. J. Appl. Stat., vol. 5, no. 1, p. 31, May 2022, doi: 10.13057/ijas.v5i1.56825.

A. W. T. Hadiwibowo, F. P. Nabilla, and A. Y. P. Yusuf, “Analisis Tingkat Kepuasan Pengguna Shopee Bedasarkan Rating Dan Ulasan Google Play Store Menggunakan Naïve Bayes,” J. Ris. Inform. Dan Teknol. Inf., vol. 1, no. 2, pp. 43–47, Mar. 2024, doi: 10.58776/jriti.v1i2.122.

S.-C. Necula, “Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior,” Behav. Sci., vol. 13, no. 6, p. 439, May 2023, doi: 10.3390/bs13060439.

Rahel Lina Simanjuntak, Theresia Romauli Siagian, Vina Anggriani, and Arnita Arnita, “Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Shopee Dengan Menggunakan Algoritma Naïve Bayes,” J. Tek. Mesin Elektro Dan Ilmu Komput., vol. 3, no. 3, pp. 23–39, Nov. 2023, doi: 10.55606/teknik.v3i3.2411.

S. Watmah, S. Suryanto, and M. Martias, “Komparasi Metode K-NN, Support Vector Machine Dan Random Forest Pada E-Commerce Shopee,” INSANtek, vol. 2, no. 1, pp. 15–21, Jun. 2021, doi: 10.31294/instk.v2i1.419.

N. Al Maeni, N. Suarna, and W. Prihartono, “ANALISIS SENTIMEN PENGGUNA SHOPEE BERDASARKAN DATA TWEET DARI TWITTER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER,” JATI J. Mhs. Tek. Inform., vol. 8, no. 2, pp. 1834–1840, Apr. 2024, doi: 10.36040/jati.v8i2.8298.

F. Adel Ramadhan, Rd. R. Permana Ruslan, and A. Zahra, “Sentiment Analysis Of E-Commerce Product Reviews For Content Interaction Using Machine Learning,” Cakrawala Repos. IMWI, vol. 6, no. 1, pp. 207–220, Feb. 2023, doi: 10.52851/cakrawala.v6i1.219.

I. Saputra et al., “Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning,” Fakt. Exacta, vol. 13, no. 4, p. 200, Feb. 2021, doi: 10.30998/faktorexacta.v13i4.7074.

E. H. Muktafin, K. Kusrini, and E. T. Luthfi, “Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Eksplora Inform., vol. 10, no. 1, pp. 32–42, Sep. 2020, doi: 10.30864/eksplora.v10i1.390.




DOI: https://doi.org/10.24167/proxies.v9i2.14046

Copyright (c) 2026 Proxies : Jurnal Informatika



View My Stats