Sentiment Analysis About Legislative Elections using Deep Learning with LSTM and CNN Models - Angraini, Nadya Arda; Lhaksmana, Kemas Muslim (Forum Kerjasama Pendidikan Tinggi, 2024)
The election of legislative members is a significant moment from the perspective of democracy, influencing the policies and direction of a country. In the digital era, sentiment analysis regarding the election of legislative members through social media has become increasingly important for analyzing public opinions and providing insights into how people respond to and feel about candidates, parties, or specific issues. The authors of this study employ deep learning methods, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models, for sentiment analysis related to legislative member elections. These models were developed and trained using preprocessed datasets. The aim of this research is to identify the highest accuracy values of the LSTM and CNN models and to analyze and classify public sentiment regarding the 2024 DPR member election.The results of this study indicate that deep learning methods can provide valuable insights into public sentiment during the 2024 legislative elections. Using a CNN model with a data ratio of 80:20, the proposed model can categorize and identify sentiments with the highest testing accuracy. It is clear that the data ratio, which provides an optimal balance between training and testing data, has a significant impact on model performance. As a result, the CNN model achieves the best results, with an accuracy of 93.27%, an F1 score of 93.19%, precision of 93.52%, and recall of 92.73. This research makes an important contribution by applying the CNN model, which succeeded in achieving the best results in categorizing sentiment, demonstrating the highest test accuracy in analyzing public sentiment towards the 2024 DPR member elections.