Comparison of Random Forest and LSTM for Tokopedia Sentiment Analysis - Saputra, Fahrizal Denta; Budiman, Fikri (Journal of Applied Informatics and Computing, 2026)
Tokopedia is one of the largest e-commerce platforms in Indonesia, where every transaction generates user reviews containing opinions about the products or services received. These reviews provide important information about product quality, but the very large quantity makes manual analysis inefficient. This study aims to automatically classify Tokopedia review sentiment and compare the performance of machine learning and deep learning methods. The dataset used was obtained from Kaggle and has undergone an initial cleaning stage, including removing irrelevant columns and manually labeling into two sentiment classes, positive and negative. The research methodology includes several stages, namely data preprocessing (cleaning, case-folding, stopword removal, tokenization, normalization, and stemming), feature extraction using TF-IDF for Random Forest and word embedding for LSTM, implementation of Random Forest and Long Short-Term Memory (LSTM) models, and model evaluation using confusion matrix. Experimental results show that LSTM provides the best performance with 94% accuracy, while Random Forest achieves 92% accuracy. These findings indicate that LSTM is more effective in understanding language context, resulting in more accurate sentiment classification and is useful for decision making in the e-commerce field.