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All Journal Elkom: Jurnal Elektronika dan Komputer JPF : JURNAL PENDIDIKAN FISIKA MONSU'ANI TANO Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi dan Komunikasi Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Kolaboratif Sains Journal of Islamic Education Management Al-Manahij : Jurnal Kajian Hukum Islam INFORMATIKA Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Journal of Technology Informatics and Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Ilmiah Sistem Informasi Proceeding International Conference on Education Al-Hayat: Journal of Islamic Education
Munifah
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Journal : Journal of Technology Informatics and Engineering

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HYBRID MODEL MACHINE LEARNING FOR DETECTING HOAXES Budi Hartono; Munifah; Sindhu Rakasiwi
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.142

Abstract

Unlimited availability of content provided by users on social media and websites facilitates aggregation around a broad range of people's interests, worldviews, and common narratives. However, over time, the internet, which is a source of information, has become a source of hoaxes. Since the public is commonly flooded with information, they occasionally find it difficult to distinguish misinformation disseminated on net platforms from true information. They may also rely massively on information providers or platform social media to collect information, but these providers usually do not verify their sources. The purpose of this research is to propose the use of machine learning techniques to establish hybrid models for detecting hoaxes. The research methodology used here is a feature extraction experiment, in which a series of features will be analyzed and grouped in an experiment to detect hoax news and hoax, especially in the political sphere by considering five modalities. The outcome of this research indicates that the relation between publisher Prejudice and the attitude of hyper-biased news sources makes them more possible than other sources to spread illusive articles, besides that the correlation between political Prejudice and news credibility is also very strong. This shows that the experiment using a hybrid model to detect hoaxes works. well. To achieve even better results in future research, it is highly recommended to analyze user-based features in terms of attitudes, topics, or credibility.
Co-Authors Ahmad Ashifuddin Aqham Ana, Ana Imroatul Mufidata Aprilia, Try Heni Aslamiyah, Mughiz Syuwaibatul Bagus Sudirman budi hartono Dalila Haji Daud Daud, Dalila Haji Hamida, Ulfi Dina Haris Ihsanil Huda Hasan Hasan Ira Yuliani Santoso Jandra , Mifedwil Laili, Ummiy Fauziyah Laksamana Rajendra Haidar Azani Fajri Mardi Lestari Micha Felayati Silalahi Miftahurrohman Miftahurrohman Mulyo, Mufrod Teguh Nurwahyuni Rabiatul Adawiyah Rifadhiyah Hikmah Sigit Umar Anggono Sindhu Rakasiwi Siswanto, Edy Slez Nazy Tiara Putri Alisa Wulandari, Ratna Wahyu Yulianti Yusal Zahro, Anisyarofatu