Sentiment Analysis of Indonesian Presidential Candidate 2024 on Facebook

Fardhan Saifullah Fattah(1), Chanifah Indah Ratnasari(2*),

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

Abstract


The 2024 Indonesian Presidential Election has become a hot topic in daily life, including on Facebook. Everyone has the freedom to express or speak their opinions on the Indonesian presidential candidate for 2024. As a result, there are a variety of viewpoints, some of which are positive and some of which are negative. This research aims to classify positive and negative sentiments in each presidential candidate's Facebook post comment data. Several processes are involved in this sentiment analysis process, including data scraping, data preprocessing, data labeling, and data classification using the Support Vector Machine (SVM) approach. The data used are comments obtained from each presidential candidate's Facebook page between July 1 and July 31, 2023. Anies Baswedan, with a positive sentiment value of 88.20% and a negative sentiment value of 11.80%, is the presidential candidate with the highest positive sentiment and the lowest negative sentiment. The SVM approach with a linear kernel produced the best precision value for positive sentiment, namely 94%.


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References


D. M. Liando, “Pemilu dan Partisipasi Politik Masyarakat (Studi Pada Pemilihan Anggota Legislatif Dan Pemilihan Presiden Dan Calon Wakil Presiden Di Kabupaten Minahasa Tahun 2014),” J. LPPM Bid. EkoSosBudKum, vol. 3, no. 2, pp. 14–28, 2016.

I. Permata Sari, “Keberpihakan Media Dalam Pemilihan Presiden 2014,” J. Penelit. Komun., vol. 21, no. 1, pp. 73–86, 2018, doi: 10.20422/jpk.v21i1.492.

A. Salam, J. Zeniarja, and R. S. U. Khasanah, “Analisis Sentimen Data Komentar Sosial Media Facebook Dengan K-Nearest Neighbor (Studi Kasus Pada Akun Jasa Ekspedisi Barang J&T Ekpress Indonesia),” Pros. SINTAK, pp. 480–486, 2018.

A. Rachmat C and Y. Lukito, “Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes,” J. Inform. dan Sist. Inf. Univ. Ciputra, vol. 02, no. 02, pp. 26–34, 2016, [Online]. Available: https://github.com/sastrawi/sastrawi

D. A. Ramadhan, Y. Nurhadryani, I. Hermadi, S. Srolwlfdo, and S. Fdqglgdwhv, “&dpsdljq $qdovlv ri 6rfldo 0hgld 8wlol]dwlrq lq -dnduwd /hjlvodwlyh (ohfwlrq,” pp. 102–107, 2014.

E. B. Santoso and A. Nugroho, “Analisis Sentimen Calon Presiden Indonesia 2019 Berdasarkan Komentar Publik Di Facebook,” Eksplora Inform., vol. 9, no. 1, pp. 60–69, 2019, doi: 10.30864/eksplora.v9i1.254.

N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 1, pp. 47–58, 2021, doi: 10.28932/jutisi.v7i1.3216.

P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.

A. Rahmansyah, O. Dewi, P. Andini, T. Hastuti, P. Ningrum, and M. E. Suryana, “Membandingkan Pengaruh Feature Selection Terhadap Algoritma Naïve Bayes dan Support Vector Machine,” Semin. Nas. Apl. Teknol. Inf., pp. 1–7, 2018.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.

N. O. F. Elssied, O. Ibrahim, and A. H. Osman, “A novel feature selection based on one-way ANOVA F-test for e-mail spam classification,” Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 3, pp. 625–638, 2014, doi: 10.19026/rjaset.7.299.

S. Z. Pranida, “Analisis Sentimen Kepuasan Pelanggan Pada Jasa Eskpedisi Menggunakan Bilstm Dan Bigru,” 2022, [Online]. Available: https://dspace.uii.ac.id/handle/123456789/40715%0Ahttps://dspace.uii.ac.id/bitstream/handle/123456789/40715/18523066.pdf?sequence=1

G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter,” INTEGER J. Inf. Technol., vol. 2, no. 1, pp. 32–41, 2017, doi: 10.31284/j.integer.2017.v2i1.95.

S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, p. 279, 2019, doi: 10.26418/jp.v5i3.34368.

M. Diki Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine Sentiment Analysis of Mola Application Reviews on Google Play Store Using Support Vector Machine Algorithm,” J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1–7, 2022.




DOI: http://dx.doi.org/10.30645/j-sakti.v7i2.703

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