SHODRI, HALIM (2023) TWITTER HOAX NEWS DETECTION SYSTEM USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE (SVM) METHOD. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This research focuses on the disruption caused to Twitter users by the textual transmission of incorrect information. The development of a tool to recognize bogus news inside the Twitter application is one of the tactics taken to tackle this problem. A corpus of textual information from social media may document written or spoken language use. Using Naïve Bayes and Support Vector Machine techniques, the authors of this study created a Twitter hoax detection system. The classification approach is executed using the pre-processing phase and TF-IDF word weighting until a corpus pertaining to false news is formed. Keywords: hoax, hoax detection, naïve bayes, support vector machine, tf-idf, natural language processing Penelitian ini berfokus pada gangguan yang disebabkan oleh pengguna Twitter oleh transmisi tekstual dari informasi yang salah. Pengembangan alat untuk mengenali berita palsu di dalam aplikasi Twitter adalah salah satu taktik yang diambil untuk mengatasi masalah ini. Korpus informasi tekstual dari media sosial dapat mendokumentasikan penggunaan bahasa tertulis atau lisan. Dengan menggunakan teknik Naïve Bayes dan Support Vector Machine, penulis penelitian ini membuat sistem pendeteksi hoax Twitter. Pendekatan klasifikasi dilakukan dengan menggunakan tahap pre-processing dan pembobotan kata TF-IDF hingga terbentuk korpus yang berkaitan dengan berita bohong. Kata kunci: hoax, deteksi hoax, naïve bayes, support vector machine, tfidf, natural language processing
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