Analisis Sentimen Media Sosial Pada Pengguna Twitter Terhadap Pemilu 2024 Menggunakan Metode LSTM

Elvanita Setyaningtyas(1*), Kristiawan Nugroho(2),

(1) Universitas Stikubank, Indonesia
(2) Universitas Stikubank, Indonesia
(*) Corresponding Author

Abstract


Elections are a crucial democratic procedure as they offer citizens the privilege of exercising their rights. Consequently, there is a wide range of contrasting responses on social media, particularly on Twitter. Twitter is a platform that facilitates sentiment analysis, a valuable tool for comprehending the public's perception of leaders and the topics addressed in a campaign. This analysis encompasses both positive and negative opinions, which are of particular importance for study. Sentiment analysis can be used to determine the propensity of Twitter users to publish material. This study commenced with gathering data from Twitter, followed by data modeling utilizing the LSTM technique and real-time implementation, resulting in the classification of individuals into supporters, non-supporters, and neutrals. LSTM, short for Long Short-Term Memory, is a sophisticated deep learning technique that serves as the foundational framework for the research project titled "Sentiment Analysis of Twitter Users towards the 2024 Election using the LSTM Method". LSTM has the benefit of being capable of retaining and manipulating long-term knowledge, as well as accessing and modifying past information. The objective of this study is to ascertain the sentiment analysis of Twitter users on the 2024 election, categorizing it as favorable, negative, or neutral. The study yielded an accuracy rate of 78% utilizing the LSTM approach.

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References


E. A. B. Bara, K. A. Nasution, R. Z. Ginting, And Kartini, “Penelitian Tentang Twitter,” J. Edukasi Nonform., Vol. 3, No. 2, Pp. 167–172, 2022.

D. A. Firdlous And R. Andrian, “Analisis Sentimen Publik Twitter Terhadap Pemilu 2024 Menggunakan Model Long Short Term Memory,” Sist. J. Sist. Inf., Vol. 12, No. 1, Pp. 52–60, 2023.

H. S. R. Fajar Nurdiansyah, “Strategi Branding Bandung Giri Gahana Golf Sebelum Dan Saat Pandemi Covid-19,” Vol. 2, No. 2, Pp. 2588–2593, 2021.

M. U. Albab, Y. Karuniawati, And M. N. Fawaiq, “Optimization Of The Stemming Technique On Text Preprocessing President 3 Periods Topic,” J. Transform., Vol. 20, No. 2, Pp. 1–10, 2023, [Online]. Available: Https://Journals.Usm.Ac.Id/Index.Php/Transformatika/■Page1

S. Khairunnisa, A. Adiwijaya, And S. Al Faraby, “Pengaruh Text Preprocessing Terhadap Analisis Sentimen Komentar Masyarakat Pada Media Sosial Twitter (Studi Kasus Pandemi Covid-19),” J. Media Inform. Budidarma, Vol. 5, No. 2, P. 406, 2021, Doi: 10.30865/Mib.V5i2.2835.

A. Najlaa, A. Triayudi, And I. Diana, “Analisis Sentimen Pada Twitter Berbahasa Indonesia Terhadap Penurunan Performa Layanan Indihome Dan Telkomsel Sentiment Analysis On Indonesian-Language Twitter Against The Decreasing Performance Of Indihome And Telkomsel Services,” Vol. 10, No. 4, Pp. 387–394, 2022, Doi: 10.26418/Justin.V10i4.50858.

L. Ardiani, H. Sujaini, And T. Tursina, “Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan Di Kota Pontianak,” J. Sist. Dan Teknol. Inf., Vol. 8, No. 2, P. 183, 2020, Doi: 10.26418/Justin.V8i2.36776.

M. P. Purba And Y. T. Wijaya, “Analisis Basic Emotion Masyarakat Pada Masa Pandemi Covid-19 Di Media Sosial Twitter Dengan Metode Lstm-Fasttext,” Semin. Nas. Off. Stat., Vol. 2022, No. 1, Pp. 643–654, 2022, Doi: 10.34123/Semnasoffstat.V2022i1.1524.

M. F. Naufal And S. F. Kusuma, “Analisis Sentimen Pada Media Sosial Twitter Terhadap Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat Berbasis Deep Learning,” J. Edukasi Dan Penelit. Inform., Vol. 8, No. 1, P. 44, 2022, Doi: 10.26418/Jp.V8i1.49951.

L. T. M. Lstm, “Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory ( Lstm ),” Pp. 55–67, 2019.

A. Santoso And G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,” Emit. J. Tek. Elektro, Vol. 18, No. 1, Pp. 15–21, 2018, Doi: 10.23917/Emitor.V18i01.6235.

L. Wiranda And M. Sadikin, “Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma,” J. Nas. Pendidik. Tek. Inform., Vol. 8, No. 3, Pp. 184–196, 2019.

Y. Astuti, I. R. Wulandari, A. R. Putra, And N. Kharomadhona, “Naïve Bayes Untuk Prediksi Tingkat Pemahaman Kuliah Online Terhadap Mata Kuliah Algoritma Struktur Data,” J. Edukasi Dan Penelit. Inform., Vol. 8, No. 1, P. 28, 2022, Doi: 10.26418/Jp.V8i1.48848.

H. Hafid, “Penerapan K-Fold Cross Validation Untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor Pada Data Kasus Covid-19 Di Indonesia,” J. Math., Vol. 6, No. 2, Pp. 161–168, 2023, [Online]. Available: Http://Www.Ojs.Unm.Ac.Id/Jmathcos

J. T. Informatika Et Al., “Berdasarkan Tingkat Kepentingan Pada Prodi / Jurusan D4 Teknik Informatika Politeknik Pos Indonesia Pt Pertamina ( Persero ) Abstrak Pt . Pertamina ( Persero ),” Vol. 13, No. 2, Pp. 1–8, 2021.




DOI: http://dx.doi.org/10.30645/jurasik.v9i2.799

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v9i2.799.g774

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