Jurnal
Sentiment Analysis of Presidential Candidates in 2024: A Comparison of the Performance of Support Vector Machine and Random Forest with N-Gram Method
Recursive Journal of Informatics · Vol. 3 No. 1 (2025) · hlm. 34–42
- ISSN
- 2986-6588, 2963-5551
- Terbit
- 2025-03-28
- Penerbit
- Universitas Negeri Semarang
- Bahasa
- eng
Abstrak
Abstract. This paper conducts a sentiment analysis of presidential candidates in Indonesia's 2024 election using Twitter data. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter entries, sentiment was categorized as positive or negative. Preprocessing techniques cleaned and normalized the data, followed by labeling with the VADER lexicon. This study contributes insights into public sentiment towards presidential candidates and the effectiveness of machine learning algorithms for political sentiment analysis. Purpose: This study aims to analyze public sentiment towards presidential candidates in Indonesia's 2024 election using the N-Gram method. By employing Support Vector Machine and Random Forest algorithms, we compare their performance in sentiment analysis. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter data entries, we seek to provide insights into the electorate's perceptions and preferences, contributing to a deeper understanding of the political landscape during this crucial period. Methods/Study design/approach: The study uses Support Vector Machine (SVM) and Random Forest algorithms for sentiment analysis on a dataset of 8555 tweets about Indonesia’s 2024 presidential candidates. SVM, paired with TF-IDF, and Random Forest, paired with N-Gram, are used for feature extraction. The data is labeled using the Vader lexicon. Result/Findings: The study compared Support Vector Machine (SVM) with TF-IDF and Random Forest with N-Gram methods in analyzing public sentiment towards Indonesia's 2024 presidential candidates. Results showed Random Forest with N-Gram achieved 85% accuracy, outperforming SVM with TF-IDF at 82%. Novelty/Originality/Value: This study provides insights into sentiment analysis applied to the 2024 Indonesian presidential election, enhancing understanding of public sentiment dynamics. Comparing SVM with TF-IDF and Random Forest with N-Gram contributes to the field, suggesting avenues for future research such as integrating contextual information or social network analysis for deeper insights into political opinion trends.
Kata kunci
Jaringan kolaborasi para penulis
26 penulis · 43 tautan kepenulisan bersama · 5 lintas organisasi
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- State University of Semarang
- Janabadra University
- Cenderawasih University
- Organisasi berbeda
- Organisasi sama
- Afiliasi tidak diketahui
- Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
| Rekan penulis | Organisasi | Artikel bersama |
|---|---|---|
| Dwi Oktafia Ariyanti | Janabadra University | 4 |
| Ridwan Arifin | State University of Semarang | 2 |
| Marsi Adi Purwadi | Cenderawasih University | 1 |
| Transna Putra Urip | Cenderawasih University | 1 |
| Anan Nugroho | State University of Semarang | 1 |
| Hizkia Natanael Richardo | State University of Semarang | 1 |
| Paramesti Astagina | State University of Semarang | 1 |
| Anantha Revoislami Ariolin | State University of Semarang | 1 |
| alamsyah alamsyah | State University of Semarang | 1 |
| Yogiana Marta Dewi | State University of Semarang | 1 |
| Sarmini | State University of Semarang | 1 |
| Suprih murdyantara | State University of Semarang | 1 |
| Gilang Ramadhan | State University of Semarang | 1 |
| Nafa Fajriati | State University of Semarang | 1 |
| Luthfi Ilham Agus Pratama | State University of Semarang | 1 |
| Muhammad Nurkholis Majid | State University of Semarang | 1 |
| Edward Jhonatan | State University of Semarang | 1 |
| Arif Saifunnasrullah | State University of Semarang | 1 |
| Sahda Armandiva Usman | State University of Semarang | 1 |
| Achmat Sodikkun | State University of Semarang | 1 |
| Arif Widiyatmoko | State University of Semarang | 1 |
| Aji Purwinarko | State University of Semarang | 1 |
| Fitri Arum Sasi | State University of Semarang | 1 |
| Wahyu Hardyanto | State University of Semarang | 1 |
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