COMBINATION K-MEANS AND LSTM FOR SOCIAL MEDIA BLACK CAMPAIGN DETECTION OF INDONESIA PRESIDENTIAL CANDIDATES 2024 - Priambodo, Wisnu; Zuliarso, Eri (Jurnal Teknik Informatika (Jutif), 2024)
Social media has become the main platform for the public and political figures to voice opinions and run political campaigns. Despite its positive impact, social media also has negative impacts, particularly in the spread of Black Campaigns. This phenomenon has become critical, especially about the 2024 elections in Indonesia that target presidential candidates. Black campaigns can trigger conflict and damage the image of presidential candidates in the eyes of the public. Therefore, it is important to detect black campaigns against presidential candidates. This research develops a Black Campaign detection model using the K-means clustering algorithm and the Long Short-Term Memory (LSTM) approach. K-means is implemented to cluster text data on Twitter social media, while LSTM is used to learn word order patterns and detect text. The result is that K-means can effectively prepare the data, and classification using LSTM shows an accuracy of 90.28%. The comparison with Ensemble Learning classification model achieved an accuracy of 94.31%. Evaluation involved accuracy, precision, recall, and F1-score, with the result that Ensemble Learning was slightly superior in the evaluation matrix. However, compared to Ensemble Learning, LSTM has an advantage in understanding word order, which can be achieved by utilizing the advantages of Deep Learning Recurrent Neural Network architecture. Testing on sample data shows the similarity between LSTM and Ensemble Learning models in detecting Black Campaigns on Twitter social media post text data.