ANALISIS SENTIMEN TWITTER TERHADAP NYAMUK WOLBACHIA MENGGUNAKAN METODE LSTM DENGAN PENDEKATAN NLTK
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Abstract
Dengue Hemorrhagic Fever (DHF) is one of the major health issues in Indonesia. One of the preventive measures is the Wolbachia mosquito program. However, the implementation of this program has sparked various reactions from the public, which can be observed through social media, particularly Twitter. This study aims to analyze public sentiment towards Wolbachia mosquitoes using the Long Short-Term Memory (LSTM) method and the Natural Language Toolkit (NLTK) approach. Data was collected through a crawling process from Twitter using keywords related to "Wolbachia mosquitoes." Preprocessing was then carried out using NLTK, including tokenization, stopword removal, and stemming. The data was manually labeled into positive, negative, and neutral sentiment categories. The LSTM model was used for sentiment classification with the best parameters, including 100 neurons, a learning rate of 0.001, a sigmoid activation function, a batch size of 32, and 7 epochs. The results indicate that the LSTM model used was able to classify sentiment with an accuracy of 95%, precision of 94%, recall of 97%, and an F1-score of 95%. This demonstrates that the LSTM method with the NLTK approach is effective in analyzing public sentiment towards
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