Uncovering Key Topics in Indonesian Political Discourse Through Twitter Analysis After the 2024 Presidential Inauguration Using Clustering methods

Syarif Hidayatullah; Ulfa Siti Nuraini

doi:10.29407/intensif.v9i1.23771

Authors

Syarif Hidayatullah Universitas Negeri Surabaya https://orcid.org/0009-0002-2380-2885
Ulfa Siti Nuraini Universitas Negeri Surabaya https://orcid.org/0009-0003-7329-1873

DOI:

https://doi.org/10.29407/intensif.v9i1.23771

Keywords:

Clustering, Social Media, President and Vice President Election, Trend, Text Processing

Abstract

Background: Social media, especially Twitter, plays a key role in political discourse, shaping public opinion. In Indonesia, the 2024 presidential Inauguration , with candidates Prabowo Subianto and Gibran Rakabuming Raka, has generated significant online conversations. Understanding public sentiment and identifying key topics is urgent for analyzing and grouping these discussions, offering insights into political views. Objective: The purpose of this research is to analyze Twitter conversations surrounding the 2024 Indonesian presidential election. The goal is to identify the main topics in these conversations and assess the effectiveness of different clustering algorithms in grouping similar tweets. Methods: This study applies a quantitative approach, using a dataset of 29,905 tweets collected from October 20 to October 25, 2024. The method includes text preprocessing, such as tokenization, stemming, and word weighting. PCA is used for dimensionality reduction. The clustering algorithms K-means, DBSCAN, PAM, and Agglomerative Hierarchical are employed, with performance evaluated based on the Silhouette Score. Results: The results reveal that the Agglomerative Hierarchical Clustering algorithm with Ward linkage and two PCA components produced the highest Silhouette Score of 0.8018. The clustering identified three distinct topics: political leadership, work and collaboration, and unity. Conclusion: This research successfully identified key discussion topics in Twitter conversations about the 2024 Indonesian presidential election. The Agglomerative Hierarchical method with Ward linkage was the most effective clustering algorithm. These findings offer valuable insights into public opinion, and future studies could expand to other social media platforms or investigate the relationship between sentiment and political outcomes.

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Author Biographies

Syarif Hidayatullah, Universitas Negeri Surabaya

Data Science Department, Universitas Negeri Surabaya
Ulfa Siti Nuraini, Universitas Negeri Surabaya

Data Science Department, Universitas Negeri Surabaya

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