Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering


  • Cika Carissa Sujadi Telkom University, Indonesia
  • Yuliant Sibaroni Telkom University, Indonesia
  • Aditya Firman Ihsan Telkom University, Indonesia




Twitter, Emotion, Presidential Election, TF-IDF Vectorizer, Agglomerative Hierarchical Clustering


Over the past few years, social media has become essential for getting up-to-date information and interacting online. During presidential elections in Indonesia, Twitter has grown as a crucial platform for expressing opinions and sharing information. This study focuses on analyzing the content types and emotions of tweets related to Anies Baswedan, one of the presidential candidates. The results show a variety of discussions, including support, criticism, and discussion of policies for the 2024 presidential candidate. Clustering enables meaningful information extraction from vast Twitter data. Data were clustered using Agglomerative Hierarchical Clustering, which resulted in the identification of 10 clusters. With 4 clusters containing opinion content and 6 clusters containing information content. In addition, 6 clusters reflect excitement, 3 reflect expectations, and 1 reflect doubt. This research provides insights into the Twitter conversation around the 2024 presidential election, providing an understanding of content and emotions expressed by users.

GS Cited Analysis


Download data is not yet available.

Author Biographies

Cika Carissa Sujadi, Telkom University, Indonesia



Yuliant Sibaroni, Telkom University, Indonesia



Aditya Firman Ihsan, Telkom University, Indonesia




Abdulhafedh, A. (2021). Incorporating K-means, Hierarchical Clustering and PCA in Customer Segmentation. Journal of City and Development, 3(1), 12–30.

Belcastro, L., Branda, F., Cantini, R., Marozzo, F., Talia, D., & Trunfio, P. (2022). Analyzing voter behavior on social media during the 2020 US presidential election campaign. Social Network Analysis and Mining, 12(1).

Cahyo, P. W., & Sudarmana, L. (2021). A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform. Elinvo (Electronics, Informatics, and Vocational Education), 6(2), 166–173.

Devika, R., Revathy, S., Sai Surriya Priyanka, U., & Subramaniya Swamy, V. (2018). Survey on clustering techniques in Twitter data. Proceedings of the 2nd International Conference on Computing Methodologies and Communication, ICCMC 2018, Iccmc, 1073–1077.

Dirjen, S. K., Riset, P., Pengembangan, D., Dikti, R., & Mailoa, E. (2017). Terakreditasi SINTA Peringkat 2 Analisis Node dengan Centrality dan Follower Rank pada Twitter. Masa Berlaku Mulai, 1(3), 937–942.

FRHAN, A. J. (2017). Hierarchical Agglomerative Clustering Algorithm Based Real-Time Event Detection from Online Social Media Network. Pdfs.Semanticscholar.Org, 13, 215–222.

Irawan, E., Mantoro, T., Ayu, M. A., Bhakti, M. A. C., Yogi, I. K., & Permana, T. (n.d.). Analyzing Reactions on Political Issues in Social Media Using Hierarchical and K-Means Clustering Methods.

Mardianti, S., Zidny, M., & Hidayatulloh, I. (2018). Ekstraksi tf-Idf n-gram dari komentar pelanggan produk smartphone pada website e-commerce. Seminar Nasional Teknologi Informasi Dan Multimedia, 6(April), 79–84.


NM, E. (2016). A Typology of Voters: Creating Voters’ Profiles via Clustering. Journal of Political Sciences & Public Affairs, 4(2), 2–7.

Purnamasari, I., & Fidia Deny Tisna Amijaya, dan. (2022). Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward (Studi Kasus : Kemiskinan Di Provinsi Kalimantan Timur Tahun 2018) Comparison Of Cluster Analysis Results Using Average Linkage Method And Ward Method (Case Stud. Jurnal EKSPONENSIAL, 13(1), 9–18.

Sinha, P., Dey, L., Mitra, P., & Thomas, D. (2020). A Hierarchical Clustering Algorithm for Characterizing Social Media Users. The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, 353–362.

Vijaya, V., Sharma, S., & Batra, N. (2019). Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 568–573.

Wulandari, N. A., Pratiwi, H., & Handayani, S. S. (2023). Perbandingan Metode K-means and Agglomerative Nesting untuk Clustering Data Digital Marketing di Twitter. 2, 189–194.

Yusup, A. H., & Maharani, W. (2021). Pembangunan Model Prediksi Kepribadian Berdasarkan Tweet Dan Kategori Kepribadian Big Five Dengan Metode Agglomerative Hierarchical Clustering. 1(1), 2021.

Zahrotun, L. (2015). Analisis Pengelompokan Jumlah Penumpang Bus Trans Jogja Menggunakan Metode Clustering K-Means Dan Agglomerative Hierarchical Clustering (Ahc). Jurnal Informatika, 9(1), 1039–1047.

Zahrotun, L., Linarti, U., Harli, B., Suandi, T., & Kurnia, H. (2023). Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students ’ Data Grouping. 7(June), 446–454.

Zhou, S., Xu, Z., & Liu, F. (2017). Method for Determining the Optimal Number of Clusters Based on Agglomerative. 28(12), 3007–3017.


Crossmark Updates

How to Cite

Sujadi, C. C., Sibaroni, Y. ., & Ihsan, A. F. . (2023). Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1230-1237.