CORRELATION ANALYSIS OF SENTIMENT OF 2024 ELECTION RESULTS AND STOCK MOVEMENTS OF POLITICAL ACTORS IN INDONESIA - Sari, Enjelita; Afuan, Lasmedi; Permadi, Ipung; Maryanto, Eddy; Rahayu, Swahesti Puspita (Jurnal Teknik Informatika (Jutif), 2024)
General elections (elections) are one of the crucial moments in the political life of a country, where the public democratically elects leaders and their deputies to manage the government. Public sentiment towards the results of elections significantly impacts the political stability and economic conditions of a country. This research aims to analyze the relationship between public sentiment towards the 2024 General Elections in Indonesia and changes in the stock prices of political actors using technological approaches and data analysis. The Long Short-Term Memory (LSTM) method is used to classify sentiment based on Twitter data collected with Harvest Tweet. Evaluation of the LSTM model shows an accuracy rate of 90%, precision of 93.6%, and recall of 92.7%. The correlation analysis using the Spearman coefficient indicates a significant negative relationship with a coefficient of 0.402 and a p-value of 0.046. Implementation of an interactive dashboard using Streamlit facilitates visualization of the data used in this study. Recommendations include increasing the amount of training data for sentiment models, exploring alternative correlation methods for deeper analysis, and refining the interface and data integration on the dashboard to enhance user experience and analysis accuracy. This research is expected to contribute to understanding the dynamics of public sentiment and its impact on the stock market in the context of Indonesian politics.