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All Journal Jurnal Ilmu Ternak Jurnal Ekonomi Modernisasi Jurnal NERS Jurnal Tataloka Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Otoritas : Jurnal Ilmu Pemerintahan EKONOMIS : Journal of Economics and Business Applied Information System and Management Media Ilmu Kesehatan JURNAL PENDIDIKAN TAMBUSAI Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam EQIEN - JURNAL EKONOMI DAN BISNIS Jurnal Keperawatan Komprehensif (Comprehensive Nursing Journal) Journal of Islamic Civilization JURNAL LENTERA BISNIS Jurnal Perencanaan Pembangunan Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Community Development Journal: Jurnal Pengabdian Masyarakat Belantika Pendidikan Media Kontak Tani Ternak Dakwah: Jurnal Kajian Dakwah dan Kemasyarakatan Jurnal Teknologi dan Sistem Tertanam Journal of Soft Computing Exploration Journal of Language and Health Jurnal Keperawatan Klinis dan Komunitas (Clinical and Community Nursing Journal) Peradaban Journal of Religion and Society Eduvest - Journal of Universal Studies Jurnal Keperawatan Soedirman (The Soedirman Journal of Nursing) Innovative: Journal Of Social Science Research Jurnal Mediakita : Jurnal Komunikasi dan Penyiaran Islam Jurnal Berita Ilmu Keperawatan Jurnal Informatika: Jurnal Pengembangan IT International Journal of Economics, Business and Innovation Research At-Tahdzib: Jurnal Studi Islam dan Muamalah Malahayati International Journal of Nursing and Health Science Jurnal Pendidikan Progresif Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Syahirul Alim
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Applied Information System and Management

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Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine Nurkholis, Andi; Styawati, Styawati; Alim, Syahirul; Saputra, Hendi; Ferriyan, Andrey
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42911

Abstract

The Indonesian government's implementation of a booster vaccination program as part of its COVID-19 response has generated diverse public reactions, particularly on social media platforms like Twitter. This study aims to analyze public sentiment regarding booster vaccines by examining Twitter data to understand the prevailing discourse and attitudes toward this policy. The research employs sentiment analysis, a text mining and processing technique, to classify tweets into positive, neutral, and negative categories. The study utilizes the Support Vector Machine (SVM) algorithm, evaluating its performance through a multi-class parameter assessment. Two multi-class strategies, One-against-one (OAO) and One-against-rest (OAR) are combined with various kernels (Sigmoid, Polynomial, and RBF) to identify the most accurate model for sentiment classification. The results show that the OAO method with the RBF kernel achieves the highest accuracy of 96%, outperforming other combinations like OAO with Polynomial (95.2%) and Sigmoid (93.7%) kernels. Similarly, the RBF kernel performs best with 95.5% accuracy in the OAR approach. Using the optimal model, sentiment analysis classifies 49 tweets as positive, 927 as neutral, and 24 as negative, revealing a predominantly neutral public sentiment with limited positive and negative opinions. In conclusion, this study demonstrates the effectiveness of SVM, particularly the OAO method with the RBF kernel, for sentiment analysis of social media data. The findings provide insights into public perceptions of the booster vaccine program, offering policymakers a data-driven basis for designing targeted communication strategies to address concerns and enhance public acceptance.
Co-Authors Adi Heru Sutomo Adjat Sudradjat Masdar Agus Wahyudi Al Faroqi, M. Ibnu Alfrisa, Bagus Amanda, Feby Amsya, Akmal Huda Maulana Andi Nurkholis Andrey Ferriyan, Andrey Anggita, Rizki Devi Anjarie Dharmastuti Anwari Bagas Setiawan, Bagas Bagus Riyono Budiman, Rizky Ayuna Dian Mutiara, Dian Edi Suharyadi Ema Madyaningrum Faizatiwahida, Novia Faranita, Dhea Fibriyanti, Efi Firmantoro, Verdy Fithriyyah, Yayu Nidaul Fitrini, Anita Happy Indah Kusumawati Hari Kusnanto Haryani Haryani Hasni Arief Hatta, Haslindar Hermawan Hermawan Hermawan, Romy Intansari Nurjannah Irnu, Julfilkar Adnan Ismail, Izudin Jamil, Sobran Julian, Muhammad Waskita Khusnudin Khusnudin, Khusnudin Kusuma, Rahmah Yanita Lidya, Herlin Lilis Nurlina Linda Herlina M. Ali Mauludin M. Nanang Choiruddin Marina Sulistyati Marina, Marina Sulistyati Mauludi, Moch. Ali Mauludin, Mohamad Ali Mauludin, MuhamadAli Mida Ainul Ghurroh Muhammad Djordan Al Sulthoni Muhammad Irawan Saputra Muninggar, Anggis Yustika Permadi, Angga Sukmara Christian Phuspa, Sisca Mayang Prakoso, Adi Buyu Prantama, Muhammad Barqah Pratama, Ariski Pusparini Anggita Ayuningtyas Rachmat Kriyantono Rahayu, Desti Rini, Sri Setiya Rohimah, Annisa'i S. Samsugi Saputra, Arif Ragil Saputra, Hendi Sari, Yohana Magda Satrya, Alan Shahril Anuar Abdul Ghalim Sibyan, Istifada Alhidayatus SRI SETIYARINI, SRI Styawati, Styawati Sugeng Winaryanto Sulistiyawati, Marina Syaiful Eddy Syamsul Rizal Uki Noviana Unang Yunasaf Utami Utami Utmiati, Nur Aulia Kusuma Vivin Maharani Ekowati Widastri, Amanda Rahmat Yazid Basthomi Yusli Effendi Zaim Mukaffi ZURAIDAH ZURAIDAH