<![CDATA[This study aims to apply the Long Short-Term Memory Networks (LSTM) with Temporal Attention method in predicting tourism density in West Java tourist destinations. The problem faced is the uncertainty in estimating tourist density at various locations and times, which makes the management of tourism resources and facilities difficult. Therefore, this study is important to provide a tool that can help make more effective decisions in the tourism sector in West Java. The urgency of this study lies in the need for accurate and real-time tourist density predictions to support the management and development of tourist destinations in West Java. With the right prediction model, related parties can regulate capacity, optimize services, and avoid negative impacts such as excess capacity and crowds that have the potential to endanger visitors and the environment. The purpose of this study is to develop a tourism density prediction model that combines the distinctive features of LSTM with a temporal attention mechanism. This model aims to provide accurate and dynamic tourist density estimates, taking into account the temporal patterns of tourist visits in West Java. The model evaluation methods used in this study are RMSE and MAE, and the results of the model testing are that it has an RMSE value of 32208867.139 and an MAE value of 5099.219, and it is hoped that there will be a dataset with a long period after the covid mass where the dataset is free from abnormal events so that a more appropriate model is obtained.]]>
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