Analisis Sentimen Ulasan Pengguna Aplikasi Shopee Indonesia Menggunakan Algoritma Random Forest

Abstract

Shopee is one of the most widely used e-commerce applications in Indonesia, and the reviews written by its users on the Google Play Store contain valuable information about service quality, application performance, and customer satisfaction. This study aims to classify the sentiment of Indonesian-language reviews of the Shopee application using the Random Forest algorithm. A total of 5,000 reviews were collected through web scraping, labeled based on user ratings, and processed through cleaning, case folding, slang-word normalization, tokenization, stopword removal, and stemming. Feature extraction was performed using Term Frequency-Inverse Document Frequency (TF-IDF), and the Synthetic Minority Over-sampling Technique (SMOTE) was applied to handle class imbalance in the training data. The experimental results show that the best Random Forest model, with 200 trees, achieves an accuracy of 89.34%, a precision of 89.40%, a recall of 88.30%, and an F1-score of 88.75%, outperforming Naive Bayes, Support Vector Machine, and K-Nearest Neighbor as comparison models. The analysis of feature importance shows that positive sentiment is dominated by words related to delivery speed and price, while negative sentiment is dominated by complaints about system errors, sellers, and refund processes. These findings can be used by application managers to prioritize service improvements.