Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024

Authors

  • Zairy Cindy Dwinnie Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru http://orcid.org/0000-0003-1532-2742
  • Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

DOI:

https://doi.org/10.30865/mib.v8i2.7504

Keywords:

Text Mining, 2024 Presidential Election Debate, Support Vector Machine, Random Forest, Logistic Regression

Abstract

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.

Author Biographies

Zairy Cindy Dwinnie, Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

Mahasiswa Program Studi Sistem Informasi

Rice Novita, Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

Dosen Program Studi Sistem Informasi

References

A. Saputra and Ubaidullah, “Analisis Kegagalan Marketing Politik Partai NasDem Pada Pemilu Legisilatif Tahun 2019 Di Ace,†Jurnal Ilmiah Mahasiswa FISIP Unsyiah, vol. 6, no. 2, May 2021.

W. Budiharto and M. Meiliana, “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis,†J Big Data, vol. 5, no. 1, Dec. 2018, doi: 10.1186/s40537-018-0164-1.

A. Putri Nardilasari, A. Lia Hananto, S. Shofia Hilabi, and B. Priyatna, “Terakreditasi SINTA Peringkat 3 Analisis Sentimen Calon Presiden 2024 Menggunakan Algoritma SVM Pada Media Sosial Twitter,†JOINTECS (Journal of Information Technology and Computer Science, vol. 7, no. 1, pp. 11–18, Mar. 2023.

Fajlurrahman Jurdi, Pengantar Hukum Pemilihan Umum, Edisi Pertama. Jakarta: Kencana, 2018.

Fathir, “Analisis Sentimen Artikel Berita Pemilu Berbasis Metode Klasifikasi,†Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, 2023.

D. Wiryany, S. Natasha, and R. Kurniawan, “PERKEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIKASI TERHADAP PERUBAHAN SISTEM KOMUNIKASI INDONESIA,†Jurnal Nomosleca, Oktober, vol. 8, no. 2, pp. 242–252, Oct. 2022.

R. Putra, “Penerapan Metode SVM Pada Klasifikasi Sentimen Terhadap Anies Baswedan SebagaiBakal Calon Presiden 2024,†Jurnal Informatika Universitas Pamulang, vol. 8, no. 2, pp. 145–152, Jun. 2023.

F. Augustinah and Widayati, “PEMANFAATAN MEDIA SOSIAL SEBAGAI SARANA PROMOSI MAKANAN RINGAN KRIPIK SINGKONG DI KABUPATEN SAMPANG,†Jurnal Dialektika, vol. 4, no. 2, pp. 1–20, Sep. 2019.

G. A. BUNTORO, R. ARIFIN, G. N. SYAIFUDDIIN, A. SELAMAT, O. KREJCAR, and H. FUJITA, “Implementation of a Machine Learning Algorithm for Sentiment Analysis of Indonesia‘s 2019 Presidential Election,†IIUM Engineering Journal, vol. 22, no. 1, pp. 78–92, 2021, doi: 10.31436/IIUMEJ.V22I1.1532.

P. Yuliandri, J. R. Fatmawati No, P. Labu, and J. Selatan, “Kampanye Partai Nasdem Pasca Deklarasi Anies Baswedan di Instagram Nasdem Party Campaign on Instagram Post Anies Baswedan Declaration,†PROMEDIA, (PUBLIC RELATION DAN MEDIA KOMUNIKASI), vol. 9, no. 1, pp. 30–55, Jun. 2023.

H. Tsaniya, R. Rosadi, and A. S. Abdullah, “Sentiment analysis towards Jokowi`s government using twitter data with convolutional neural network method,†in Journal of Physics: Conference Series, IOP Publishing Ltd, Jan. 2021. doi: 10.1088/1742-6596/1722/1/012017.

A. Ziruddin, K. Rahman, and M. A. Maulidi, Merawat Negara Hukum. Guepedia, 2023.

D. Tata Lukmana, S. Subanti, Y. Susanti, and P. Studi Statistika, “ANALISIS SENTIMEN TERHADAP CALON PRESIDEN 2019 DENGAN SUPPORT VECTOR MACHINE DI TWITTER,†in Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019 UMT, 2019, pp. 154–160.

I. F. Fanhar Nur, A. Herdiani, and W. Astuti, “Analisis Sentimen Berbasis Leksikon InSet Terhadap Partai Politik Peserta Pemilu 2019 Pada Media Sosial Twitter,†in e-Proceeding of Engineering, Dec. 2019, pp. 10397–10407.

R. A. Husen, R. Astuti, L. Marlia, R. Rahmaddeni, and L. Efrizoni, “Analisis Sentimen Opini Publik pada Twitter Terhadap Bank BSI Menggunakan Algoritma Machine Learning,†MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 211–218, Oct. 2023, doi: 10.57152/malcom.v3i2.901.

C. Chazar and B. E. Widhiaputra, “Machine Learning Diagnosis Kanker Payudara Menggunakan Algoritma Support Vector Machine,†INFORMASI (Jurnal Informatika dan Sistem Informasi), vol. 12, no. 1, pp. 67–80, May 2020.

M. R. Adrian, M. P. Putra, M. H. Rafialdy, and N. A. Rakhmawati, “Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB,†JURNAL INFORMATIKA UPGRIS, vol. 7, no. 1, pp. 36–40, 2021.

D. Gunawan, D. Riana, D. Ardiansyah, F. Akbar, and S. Alfarizi, “Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023,†Jurnal Teknik Komputer AMIK BSI, vol. 6, no. 1, pp. 121–129, Jan. 2020, doi: 10.31294/jtk.v4i2.

A. Maulana, Inayah Khasnaputri Afifah, Asghafi Mubarrak, Kiagus Rachmat Fauzan, Ardhan Dwintara, and B. P. Zen, “COMPARISON OF LOGISTIC REGRESSION, MULTINOMIALNB, SVM, AND K-NN METHODS ON SENTIMENT ANALYSIS OF GOJEK APP REVIEWS ON THE GOOGLE PLAY STORE,†Jurnal Teknik Informatika (Jutif), vol. 4, no. 6, pp. 1487–1494, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.863.

M. Wahyudin, “Analisis Sentimen pada Data Twitter untuk Memprediksi Hasil Pemilihan Umum,†JUCA (Jurnal Cyber Area), vol. 3, no. 3, pp. 1–18, 2023.

E. Nur, “STRATEGI KOMUNIKASI TIM SUKSES PADA KAMPANYE POLITIK UNTUK MEMENANGKAN CALON LEGISLATIF KOTA MAKASSAR The Communication Strategy of Success Team in Political Campaign to Win Legislative Candidates in Makassar,†Jurnal Diakom |, vol. 2, no. 1, pp. 120–128, Sep. 2019.

A. Hossain, M. Karimuzzaman, M. M. Hossain, and A. Rahman, “Text mining and sentiment analysis of newspaper headlines,†Information - MDPI, vol. 12, no. 10, pp. 1–15, Oct. 2021, doi: 10.3390/info12100414.

D. Pramana, M. Afdal, and I. Permana, “Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors,†JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, pp. 1–10, 2023, doi: 10.30865/mib.v5i1.2293.

W. Silalahi and A. Hartanto, “Klasifikasi Sentimen Support Vector Machine Berbasis Optimasi Menyambut Pemilu 2024,†JRST (Jurnal Riset Sains dan Teknologi), vol. 7, no. 2, p. 245, Sep. 2023, doi: 10.30595/jrst.v7i2.18133.

F. F. Abdulloh and I. R. Pambudi, “Analisis Sentimen Pengguna Youtube Terhadap Program Vaksin Covid-19,†CSRID (Computer Science Research and Its Development Journal), vol. 13, no. 3, p. 141, Nov. 2021, doi: 10.22303/csrid.13.3.2021.141-148.

I. Afdhal et al., “Penerapan Algoritma Random Forest Untuk Analisis Sentimen Komentar Di YouTube Tentang Islamofobia,†Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 5, no. 1, 2022.

A. R. Hidayati, A. S. Fitrani, M. A. Rosid, F. Sains, and D. Teknologi, “Analisa Sentimen Pemilu 2019 Pada Judul Berita Online Menggunakan Metode Logistic Regression,†KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), vol. 4, no. 2, pp. 298–305, Apr. 2023.

A. R. Dewi, S. Diana, M. A. Fakhrezi, N. Awang, H. Ma’arif, and D. D. Saputra, “SENTIMEN ANALISIS TERHADAP PUAN MAHARANI SEBAGAI KANDIDAT CALON PRESIDEN 2024 BERDASARKAN OPINI TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN ADABOOST,†JSiI (Jurnal Sistem Informasi), vol. 10, no. 1, pp. 75–80, Mar. 2023, doi: 10.30656/jsii.v10i1.5785.

Downloads

Published

2024-04-23

Issue

Vol. 8 No. 2 (2024): April 2024

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. 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 acknowledgment of its initial publication in this journal.
  3. 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 (Refer to The Effect of Open Access).