Sentiment Analysis of Gojek Application User Reviews Using the Long Short-Term Memory (LSTM) Algorithm

Abstract

This study was conducted to perform sentiment analysis by identifying patterns or trends in user reviews of the Gojek application using the Long Short-Term Memory (LSTM) algorithm, which was implemented in the form of a simple web-based application or dashboard. In today’s digital era, technological advancements have significantly influenced various aspects of life, particularly the mobile-based transportation service industry. One of the most widely used online transportation services in Indonesia is Gojek. It is essential for Gojek to listen to customer reviews; therefore, sentiment analysis is required to identify patterns or trends within user feedback so the application can better respond to user needs. This research utilizes the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that incorporates a cell state and gating mechanisms (input, forget, and output gates) to regulate the flow of information. This structure enables LSTM to retain relevant information while discarding irrelevant data, allowing it to capture both short-term and long-term patterns in text reviews. The model was used to analyze sentiment within a dataset collected from 2021 to 2024. The experimental results show that LSTM achieved an optimal accuracy of 78% using a 70:30 dataset split, providing balanced performance across both majority and minority classes, with a significant improvement in the f1-score for each class (0: 0.73; 1: 0.75; 2: 0.85) after applying the SMOTE technique to address class imbalance. Without SMOTE, the highest accuracy reached 83% with the same split (70:30); however, the neutral class could not be detected (f1-score = 0). With SMOTE, although accuracy slightly decreased, the overall performance became more balanced as the neutral class could be properly recognized.