<![CDATA[The relocation of the national capital (IKN) has garnered public attention, triggering various reactions and sentiments among the community. Sentiment analysis is crucial for understanding public perceptions of an issue, particularly on social media platforms like Twitter and YouTube. This study's sentiment analysis employs Word2Vec parameters, including architecture and dimensions. Additionally, hyperparameters such as the Optimizer and activation functions are applied to the Long Short-Term Memory (LSTM) model to analyze their effect on sentiment classification performance related to the IKN relocation. The study aims to compare the influence of Word2Vec parameters on LSTM model hyperparameter performance in sentiment classification. Data on the IKN relocation were gathered from tweets and YouTube video comments, then processed to form a text corpus used to train the Word2Vec model with Skip-gram and Continuous Bag-of-Words (CBOW) architectures, utilizing different dimension sizes (100 and 300) to enhance word representation in vectors. After obtaining word representations, the LSTM model was applied to classify sentiments using hyperparameters such as activation functions (ReLU, Sigmoid, and Tanh) and two Optimizers (Adam and RMSProp). The results indicate that the Skip-gram architecture tends to yield higher accuracy compared to CBOW, particularly with larger vector dimensions (300), which generally improved model accuracy, especially when using the RMSProp Optimizer and ReLU activation function, achieving an accuracy of 91%. It can be concluded that dimension values and architecture in Word2Vec, as well as the use of Optimizer and activation functions in LSTM, significantly impact model performance.]]>
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