Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.

Indonesia' $s$ presidential debate is one of the series before the 2024 presidential election to convey each candidate's vision and mission. As the debate was broadcasted on popular video streaming platforms such as YouTube, there were various public responses to the candidates and the debate in the video comments. This study will analyze the public's sentiment in the comments on the presidential debate video towards each candidate. This study will use the YouTube API to collect video comment data and will use the Support Vector Machine (SVM) algorithm as a method of classifying comments as either positive or negative sentiment. The best training and testing data split in the testing is with a ratio of 80:20. With this ratio, it will produce an accuracy score of 0.90 and an AUC value of 0.95 for the Anies dataset. In the Prabowo dataset, it produces an accuracy score of 0.85 and an AUC value of 0.92. In the Ganjar dataset, it produces an accuracy score of 0.88 and an AUC value of 0.92. Furthermore, based on the results of sentiment prediction analysis, it can be seen that positive sentiment in Anies contains support for the vision and mission, in Prabowo contains full support in the general election, and in Ganjar contains satisfaction with the track record. Meanwhile, in negative sentiment, it can be seen that in Anies, it contains distrust of Anies' plan; in Prabowo, it contains doubts about voting; in Ganjar, it contains dissatisfaction with the results of the debate.