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All Journal Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi Faktor Exacta JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) DoubleClick : Journal of Computer and Information Technology Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Nova Agustina
Sekolah Tinggi Teknologi Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Faktor Exacta

 1 
Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media Nova Agustina; Adrian Adrian; Mercy Hermawati
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.11259

Abstract

Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "satrawi". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface.
Co-Authors Aditiya Suryana Adrian, Adrian Arizal Sabila Nurhikam Azril Tazidan Octa Nuryawan Erlin Ipan Saepul Milal Junadhi Junadhi Laili Suryati M. Hasanudin M. Hasanudin Mamun Hasbullah Marlim, Yulvia Nora Mercy Hermawati Miftahul Rizal Mochammad Aliffiallathifa Nur Azhari Muchammad Naseer Muhamad Fauzan Rajab Muhammad Fadhil Muttaqin Rifki Aditya Nugraha Rivaldi Syaputra Saepul Rohman Sandi Hermawan Satrio Rully Priyambodo Slamet Risnanto Sri Erina Damayanti Supriadi, Irwin Syifa Nur Fauziah Tatang Bukhori Yanto Yanto