Analysis of Public Sentiment Towards President Prabowo's Work Program Using The CNN
DOI:
https://doi.org/10.30871/jaic.v9i4.9394Keywords:
Sentiment Analysis, Prabowo’s Work Program, Convolutional Neural Network, Confusion MatrixAbstract
Digital media has now become the primary means for Indonesians to receive and respond to information, including the work programs presented by Prabowo Subianto. One of the programs that is widely discussed by the public is related to efforts to improve the national economy. Public responses to this issue are widespread on social media, reflecting diverse sentiments. Therefore, this study aims to analyze the sentiment of comments from social media users X regarding President Prabowo's work programs in the economic sector, using a deep learning approach based on the Convolutional Neural Network (CNN) architecture. The methods employed include data collection, text preprocessing, and training a CNN model. The dataset used consisted of 2,467 data points, with 1,086 labeled as positive and 1,381 labeled as negative. The test results showed that the model achieved an accuracy of 87.45% and an Area Under the Curve (AUC) score of 0.9373, indicating excellent classification performance in distinguishing between positive and negative sentiments. This study proves that the combination of CNN and FastText is a practical approach to understanding text-based public opinion from social media.
Downloads
References
[1] P. Subianto and G. R. Raka, “Visi, Misi dan Program Calon Presiden dan Wakil Presiden 2024-2029,” Medcom.id, 2024.
[2] B. Hakim, “Analisa Sentimen Data Text Preprocessing Pada Data Mining Dengan Menggunakan Machine Learning,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, pp. 16–22, 2021, doi: 10.30813/jbase.v4i2.3000.
[3] C. F. Hasri and D. Alita, “Penerapan Metode Naive Bayes Classifier Dan Support Vector Machine Pada Analisis Sentimen Terhadap Dampak Virus Corona Di Twitter,” Jurnal Informatika dan Rekayasa Perangkat Lunak, vol. 3, no. 2, pp. 145–160, 2022, doi: 10.33365/jatika.v3i2.2026.
[4] B. Haryanto, Y. Ruldeviyani, F. Rohman, T. N. Julius Dimas, R. Magdalena, and F. Muhamad Yasil, “Facebook Analysis of Community Sentiment on 2019 Indonesian Presidential Candidates From Facebook Opinion Data,” Procedia Comput Sci, vol. 161, pp. 715–722, 2019, doi: 10.1016/j.procs.2019.11.175.
[5] N. Rezki, S. A. Thamrin, and S. Siswanto, “Sentiment Analysis of Merdeka Belajar Kampus Merdeka Policy Using Support Vector Machine With Word2Vec,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0481–0486, 2023, doi: 10.30598/barekengvol17iss1pp0481-0486.
[6] S. N. Listyarini and D. A. Anggoro, “Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 261–268, 2021, doi: 10.52436/1.jpti.60.
[7] F. M. Shiri, T. Perumal, N. Mustapha, and R. Mohamed, “A Comprehensive Overview and Comparative Analysis on Deep Learning Models.”
[8] R. N. Razzak, “Ekspansi Fitur dengan FastText untuk Analisis Sentimen di Media Sosial X Menggunakan Recurrent Neural Network dan Covlutional Neural Network,” 2025.
[9] M. Alfonso and D. B. Rarasati, “Sentiment Analysis of 2024 Presidential Candidates Election Using SVM Algorithm,” JISA(Jurnal Informatika dan Sains), vol. 6, no. 2, pp. 110–115, 2023, doi: 10.31326/jisa.v6i2.1714.
[10] H. Kim and Y. S. Jeong, “Sentiment classification using Convolutional Neural Networks,” Applied Sciences (Switzerland), vol. 9, no. 11, Jun. 2019, doi: 10.3390/app9112347.
[11] M. Umer et al., “Impact of convolutional neural network and FastText embedding on text classification,” Multimed Tools Appl, vol. 82, no. 4, pp. 5569–5585, Feb. 2023, doi: 10.1007/s11042-022-13459-x.
[12] I. N. Khasanah, “Sentiment Classification Using fastText Embedding and Deep Learning Model,” in Procedia CIRP, Elsevier B.V., 2021, pp. 343–350. doi: 10.1016/j.procs.2021.05.103.
[13] A. Rajesh and T. Hiwarkar, “Sentiment analysis from textual data using multiple channels deep learning models,” Journal of Electrical Systems and Information Technology, vol. 10, no. 1, Nov. 2023, doi: 10.1186/s43067-023-00125-x.
[14] A. Hagi and D. B. Rarasati, “Sentiment Analysis of Sirekap Application Review Using Logistic Regression Algorithm,” Jurnal Informatika, vol. 11, no. 2, pp. 55–64, 2024.
[15] J. Li, “Area under the ROC Curve has the most consistent evaluation for binary classification,” PLoS One, vol. 19, no. 12 December, Dec. 2024, doi: 10.1371/journal.pone.0316019.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Angelina Pramana Thenata, Dimas Sakti Reka Saputra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
