Presidential elections held every five years, often generates significant public discourse. The 2024 presidential
election saw the release of the documentary Dirty Vote, which raised allegations of electoral fraud and sparked
polarized opinions on social media, especially on X. This study aims to analyze public sentiment toward Dirty Vote
using geo-sentiment analysis and the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Data were collected
from geotagged tweets, with sentiment classified as positive, negative, or neutral. The research explored various data
processing techniques, including TF-IDF for feature extraction, FastText for feature expansion, and balancing
methods like SMOTE and class weighting to address data imbalance. Results showed that the baseline Bi-LSTM
model achieved an accuracy of 71.57% and an F1-Score of 74.05%. When enhanced with TF-IDF and FastText,
accuracy increased to 77.07%, though the F1-Score dropped slightly to 72.95%. Applying SMOTE resulted in a
decrease in accuracy to 76.45%, but significantly improved the F1-Score to 74.93%. Exploratory data analysis
revealed that negative sentiment was most concentrated in Java Island, particularly Jakarta, and peaked during
February 2024, coinciding with the documentary's release and the election period. This study significantly contributes
to understanding how geographic locations influence public opinion on sensitive political issues. A lack of
understanding of geographically-based sentiment patterns can hinder identifying regional needs, leading to poorly
targeted policies. By integrating data analysis methods with geographical approaches, this research provides deep
insights for designing more effective, data-driven public intervention strategies and supports policymaking that is
more responsive to the dynamics of public opinion.