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All Journal Jurnal Pseudocode PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JSAI (Journal Scientific and Applied Informatics) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Komputer, Informasi dan Teknologi International Journal of Education and Social Science (IJESS) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika JURNAL BESEMAH Cendikia Pendidikan RESLAJ: Religion Education Social Laa Roiba Journal JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) JCOSIS (Journal Computer Science and Information Systems)
Ardi wijaya
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Journal : International Journal of Information Technology and Business

 1 
Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes Ardi Wijaya; Rozali Toyib; Jestika Safitri; Anisya Sonita; Yulia Darnita
International Journal of Information Technology and Business Vol. 7 No. 1 (2024): November: International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.712024.01-08

Abstract

Twitter, a social media platform with millions of users, serves as a valuable source for unique insights. The case of Lestibillar domestic violence has garnered attention, fueling various circulating rumors that encompass positive, negative, and neutral opinions. This, in turn, gives rise to the potential spread of fake news. To counter this, sentiment analysis is employed using machine learning techniques. In this research, two machine learning algorithms within the realm of supervised learning are compared: lexicon-based and Naive Bayes. Sentiment objects are created for each algorithm to facilitate the comparison, aiming to determine which algorithm performs better in terms of accuracy. The results of the calculations indicate that Naive Bayes outperforms, achieving a superior accuracy of 99.96%, while the lexicon-based method lags significantly behind at 10.29%. The dominance of positive tweets is evident, comprising 2709 out of the total tweets on Twitter.
Co-Authors Aji Setiyadi Apridiansyah, Yovi Aripun Army Martia Harjuna Bryan Febriansyah Chandra Kusuma Johan2 Darnita, Yulia Daryono, Basofi Rachmadani Dendi Pranata, Dendi Pranata Dendy Pratama Diana Dimas Andrean Saputra Eka Sahputra Elly Hertita fadila, selvana Gading Cempaka Gunawan Guntur Alam Habiel Fachrozzy Harry Witriyono Heru Franata hidayah, agung kharisma Imelia Okta Rama Yanti Jerry Ario Zhonata Jestika Safitri Kusuma Putra Al Pharisie Lestri Yanti Liza nurpatmala M Yusuf Ramadhan Marhalim Maskuri Sutomo Metrisza Yona Saputra Mohammad Candra wiliam Muhamad Abdul Fajri Muhammad Husni Rifqo Muhammad Kurrata A’yunin Muhammad Rafi Aldan Pratama Muhammad Teguh Satrio Muntahanah, Muntahanah Nhadya Vita Loca Noris Feter Novan Gilang Ramadhan Nuri David Maria Veronika Nuri David Maria Veronika Pahrizal Pahrizal Pahrizal Pandu Sujaba Putri Rahma Della Rajes Andika Putra Ramzi, Reja Muhamad Randi Trio Ardiansyah Ridho Akbar rindy, rindy balincha Rosalina Rosalina Rozali Toyib Safitri, Aisyah Seftianto Sonita, Anisya Surya Ade Saputera Venny Arisi Vicky Victori Yoga Muhamad Aryanto Yoga Saputra Yovi Apridiyansyah Yudha, Bima Satria Yulia Darmi Yusrizal Yusrizal