Analisis Sentimen Penggunaan Artificial Intelligence Terhadap Penyelenggaraan Pemilu 2024 Menggunakan Metode LSTM-RNN
Abstract
The use of Artificial Intelligence (AI) in Indonesia's 2024 general election introduces new opportunities and challenges to the political process. AI is employed in various aspects, including political campaigns, voter data analysis, and election monitoring. While AI can enhance efficiency and accuracy, concerns about ethics, privacy, and its impact on transparency remain significant. This study aims to analyze public sentiment toward the use of AI in the 2024 election using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) approach. Data were collected from various social media platforms, with a total of 768 data points analyzed. The LSTM-RNN model achieved an accuracy of 96%, demonstrating that this approach is effective in predicting and identifying public sentiment. The findings of this study are expected to provide valuable insights for political parties, campaign teams, and election organizers to better respond to public preferences. Additionally, this research is anticipated to contribute to the development of more effective campaign strategies while maintaining transparency and the integrity of the electoral process in the digital age.
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References
M. Chinen, The International Governance of Artificial Intelligence. Edward Elgar Publishing Limited, 2023.
B. Prakoso, atul Himmah, and F. Kurnia Illahi, “Dinamika Politik Menuju Pemilihan Presiden 2024 Di Indonesia: Studi Social Network Analysis,” Jurnal Lanskap Politik, vol. 1, pp. 107–133, Sep. 2023, doi: 10.31942/jlp.2023.1.2.
A. Komarudin, A. Meutia Hilda, and C. Author, “Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Pada Play Store Menggunakan Metode Naïve Bayes,” 2024. [Online]. Available: http://jurnal.bsi.ac.id/index.php/co-science
A. Daisy, Z. Fadhilah, and S. Retnoningsih, “PERANCANGAN KAMPANYE DIGITAL MELAWAN DISINFORMASI MELALUI ARTIFICIAL INTELLIGENCE DAN DEEPFAKE DI KALANGAN PRA LANSIA USIA 45-55 TAHUN,” 2024.
I. Muhammad and M. Mirza, “Implementasi Artificial Intelligence Dalam Iklan Politik Menuju Masyarakat Indonesia 5.0,” JURNAL VISUAL IDEAS, vol. 3, no. 2, 2023.
T. Akbar, R. Imanda, I. A. Abstrak, and K. Kunci, “Perbandingan Analisis Sentimen pada Aplikasi SIREKAP dengan aplikasi SITUNG di Media Sosial X Menggunakan Algoritma Support Vector Machine,” The Indonesian Journal of Computer Science, vol. 13, no. 4, Aug. 2024, doi: 10.33022/ijcs.v13i4.4084.
G. S. N Murthy, S. Rao Allu, B. Andhavarapu, M. Bagadi, and M. Belusonti, “Text based Sentiment Analysis using LSTM; Text based Sentiment Analysis using LSTM,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 05, May 2020, [Online]. Available: www.ijert.org
L. Kurniasari and A. Setyanto, “SENTIMENT ANALYSIS USING RECURRENT NEURAL NETWORK-LSTM IN BAHASA INDONESIA,” 2020.
D. Wahyudi and Y. Sibaroni, “Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, Jun. 2022, doi: 10.47065/bits.v4i1.1665.
O. Eka Putra, “IMPLEMENTASI ARTIFICIAL INTELLIGENCE PADA SISTEM PENGAWASAN PASIEN RUMAH SAKIT,” J Teknol, vol. 10, no. 02, 2020.
E. Indrayuni and A. Nurhadi, “OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER,” INTI Nusa Mandiri, vol. 18, no. 1, pp. 65–70, Aug. 2023, doi: 10.33480/inti.v18i1.4282.
Muslih, A. Meutia Hilda, and M. Jafar Elly, “Metode Klasifikasi Support Vector Machine (SVM) Untuk Analisis Sentimen Aplikasi Bing_ Chat with AI & GPT-4 Di Google Play Store,” PETIR: Jurnal Pengkajian dan Penerapan Teknik Informatika, vol. 17, Mar. 2024, doi: 10.33322/petir.v17i1.2283.
M. A. Khder, “Web scraping or web crawling: State of art, techniques, approaches and application,” International Journal of Advances in Soft Computing and its Applications, vol. 13, no. 3, pp. 144–168, 2021, doi: 10.15849/ijasca.211128.11.
M. Z. Rahman, Y. A. Sari, and N. Yudistira, “Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM),” 2021. [Online]. Available: http://j-ptiik.ub.ac.id
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
S. Sarica and J. Luo, “Stopwords in technical language processing,” PLoS One, vol. 16, no. 8 August, Aug. 2021, doi: 10.1371/journal.pone.0254937.
J. Fehle, T. Schmidt, and C. Wolff, “Lexicon-based Sentiment Analysis in German: Systematic Evaluation of Resources and Preprocessing Techniques,” 2021. [Online]. Available: https://www.springer.com/de
I. O. Muraina, “IDEAL DATASET SPLITTING RATIOS IN MACHINE LEARNING ALGORITHMS: GENERAL CONCERNS FOR DATA SCIENTISTS AND DATA ANALYSTS,” 2022. [Online]. Available: https://www.researchgate.net/publication/358284895
N. Yudistira, Deep Learning: Teori, Contoh Perhitungan, dan Implementasi. Deepublish, 2024.
T. Szandała, “Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks,” 2020.
J. Cahyani, S. Mujahidin, and T. P. Fiqar, “Implementasi Metode Long Short Term Memory (LSTM) untuk Memprediksi Harga Bahan Pokok Nasional,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 2, p. 346, Jul. 2023, doi: 10.26418/justin.v11i2.57395.
B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich, “A survey on long short-term memory networks for time series prediction,” in Procedia CIRP, Elsevier B.V., 2021, pp. 650–655. doi: 10.1016/j.procir.2021.03.088.
M. H. Al-Areef and K. Saputra, “Analisis Sentimen Pengguna Twitter Mengenai Calon Presiden Indonesia Tahun 2024 Menggunakan Algoritma LSTM,” Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), vol. 22, pp. 270–279, Aug. 2023, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jis/index
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