Understanding Public Sentiment on Jakarta Public Transportation Using LSTM
Keywords:
sentiment analysis, mass public transportation, traffic congestion, NLP, LSTMAbstract
Traffic congestion is a major issue in Jakarta due to the number of vehicles exceeding road capacity. To address this, the Jakarta government has introduced various types of public transportation to drive commuters to shift to mass public transport from using private vehicles. However, the question remains: is the public transportation system optimal? Understanding public sentiment is crucial for optimizing the available transport options. This research aims to understand the public sentiment from social media using the LSTM method. By examining how people perceive the current state of mass public transportation, we can evaluate its effectiveness. From the approximately 1000 data from social media and google review that were processed in this research, the results show that using BiLSTM could reach an accuracy of 94% for datasets that only have Positive and Negative Sentiment and 74% for a dataset that consists of Neutral sentiment too. Ultimately, this study hopes to provide and deliver a summary of the public transportation system utilized by the NLP and LSTM algorithm, and also serve as an evaluation tool to improve services for encouraging a shift from private vehicles to public transport which can reduce traffic congestion in Jakarta.
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