Sentiment Analysis of Tokopedia Customer Reviews Using Decision Tree - Setyawan, Dimas Ari; Utomo, Prasetyo Yekti; Muarief, Ridho; Fauzi, Deni Nur; Muslima, Nur Hanifa (Journal of Development Research, 2026)
This research aims to analyze the sentiment of clothing stores on the Tokopedia marketplace. The dataset comprises the top 13 clothing stores and star ratings ranging from 1 to 5, spanning the period from 2019 to 2024. The Decision Tree algorithm was implemented for the sentiment analysis process, employing three attribute splitting methods: Gini Index, Gain Ratio, and Information Gain. A total of 10,490 reviews were utilized and categorized into three sentiment classes: 4,018 reviews with 4 and 5-star ratings were classified as the positive sentiment class; 3,378 reviews with 3-star ratings as the neutral sentiment class; and 3,094 reviews with 1 and 2-star ratings as the negative sentiment class. The results of the Decision Tree modeling demonstrate that the highest performance was achieved using the Information Gain algorithm, yielding an accuracy rate of 44.23%.