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All Journal DIKSI Jurnal Penelitian Pendidikan IPA (JPPIPA) Biota RESIPROKAL: Jurnal Riset Sosiologi Progresif Aktual Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) PROGRES PENDIDIKAN Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JOLLT Journal of Languages and Language Teaching Jurnal Teknologi Pendidikan : Jurnal Penelitian dan Pengembangan Pembelajaran Jurnal Kependidikan Lensa: Jurnal Kependidikan Fisika Jurnal Tatsqif Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) International Journal of Essential Competencies in Education International Journal of Ethnoscience and Technology in Education IJMST
Faresta, Rangga Alif
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Journal : IJMST

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The Role of Sentiment Analysis in Election Predictions Compared to Electability Surveys Firdaus, Asno Azzawagama; Faresta, Rangga Alif; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.1-8.2025

Abstract

Indonesia has just held the voting process for the Presidential Election. This has become a discussion of various media to social media, especially Twitter. However, when making predictions based on social media it will be so difficult if there is no specific technique or method for handling it. The prediction method we found in Indonesia often uses electability surveys in elections, but this research will compare it with sentiment analysis that utilizes social media in data collection. Another novelty is the data used during candidate campaign debates using the Support Vector Machine (SVM) method in class classification. The results obtained show that there are still differences between electability and sentiment, but this is due to several factors such as the amount of data, data objects, data collection time span, and methods. Overall, the SVM method has an accuracy of more than 0.75 on all three candidate datasets, proving that this method can be applied to similar cases.
Co-Authors 'Ardhuha, Jannatin ,, Karmilawati Admawati, Harsi Afuwani, Siti Hanum Agustina, Sindi Nopita Andika, Jeffri Dian Anggara, Walid APRILIANA, APRILIANA Aris Doyan Asno Azzawagama Firdaus Bahtiar Bahtiar Cahya, Ratna Chi, Yixuan David Ginola, David Devi, Leona Patria Ernasari Ernita, Nevi Fakhriyah, Novita Nurul Faroby, Muhammad Adzy Fitriani, Lina Ulfa Hikmawati*, Hikmawati Ike Lusi Meilina Jon, Roi Boy Kamal, Eta Lestari Karisma, Alan Maulana M. Zaenal Abidin Malisa, Malisa Mandiri, Tegar Agusfian Mopoliu, Abdul Ziqron Mufidah, Yuraeda Muhajir Yunus Muhammad Fachry Muhammad Luthfi MUHAMMAD TAUFIK Muliadi, Irman Murti, Baiq Dwi Mustofa, Hisbulloh Als Mutia, Nur Balqis Narayana, I Gede Perdana Putra Nicholas, Teo Zhao Ser Bryan Nila Mutia Dewi Raenaldi, Raenaldi rohmah, rohmah Safana, Mega Safitri, Ivo Yolanda Satutik Rahayu Septiawan, Adi Sinambela, Indah Astri Natalia Sofy, Peny Aulia Soni Ariawan Suhardi, Rizal M. Sukirno Sukirno Sumaeni, Baiq Suud Waly, Muchamad Muchibbuddin Warodiah, Yudia Nikmatul Yuraeda, Mufidah Yuyun Yulia ZM, Hamidsyukrie Zulkarnaen Zulkarnaen