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Journal : Journal of Technology Informatics and Engineering

 1 
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.
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 Ahmad Tirmizi Akhiroh, Puji Alex Ristanto Pratama Alfani Ghutsa Daud Amad Maijun Amanda Putri Antono Adhi Arsito Ari Kuncoro Ayyub Hamdanu Budi Nurmana Azzah Rawani Bayu Erdian Syah Christine Yossy Meinarty Claudia Alviani Danang, Danang Dedy Irawadi Dendy Kurniawan Desta Pratama Arya Adjie Devita Putri Dewi Handayani Untari Ningsih DEWI SETYANINGRUM Dhani Wahyu Wicaksono Dhimas Dita Rahadian Dian Kristiawan Nugroho Diana Safitri Edy Winarno Eko Nur Wahyudi Eko Siswanto Eko Siswanto Elfizon Amir Ella Irmayeni Emy Leonita Endrahadi Rahadian Eri Zuliarso Eva Magdelana BR. Simamora Fahar Kartiko Kuncoro Jati Farizki Ade Korsa Febrianna Vecillia Sukamto Febryantahanuji Febryantahanuji Feti Nur Rahmawati FR. Wuriningsih Fr.Wuriningsih Fujiama Diapoldo Silalahi Hadi Yusuf Hanung Eka Atmaja Hari Dwi Utami Hari Kurniawanto Hendra Deswita Hendri Rasminto Imam Husni Al Amin Iman Saufik Suasana Indra Ava Dianta Irdha Yunianto Jepisah, Doni Julitta Dewayani Juwaidah Sharifuddin Kartika Sekar Damayanti KHAIRUL IKHWAN Khoirur Rozikin Khotibul Umam Leli Afrida Lismawaty Margareta Munte M Rayhan Fadhil Rais Maksum Syahri Lubis Masnita Massaguni Mia Pertiwi Monica Rizqi Yanuar Setyowati Muhamad Rizal Rifa’i Muhammad Rian Setyawan Mujiyono Mujiyono Munifah Mutiara Anggita Saputri Nabila Exsa Tristanti Nadia Syakina Nanang Febrianto Naufal Ainun Ridho Wibowo Neni Rosnani Nike Ayu Nina Siti Salmaniah Siregar Novy Olyvia Priyo Sugeng Winarto Purwatiningtyas Purwatiningtyas Radyanto, Mohammad Riza Rany, Novita Rara Sri Artati Redjeki Renal Renal Revina Rahmadani Riauni Syaputri Rifki Arif Rizki Samur Rosmita Salsabila Nur Hapsari Samingan Santi Widiastuti Setya Permana Sutisna Shabrina Novita Dwi Utami Sindhu Rakasiwi Siti Yurika Kurniawati Sitompul, Binsar Parulian SRI LESTARI Suci Yuliawati Sulartopo Sulartopo Sulartopo Suprihono Setyawan Suriadi Nina Syah Putra, Rayhan Rifki Theresi Dwiati Wismarini Tito Yasin Hidayah Tomara Indrajaya Toni Wijanarko Adi Putra Veronica Lusiana Widya Ariyani Wismarini T.D. Yenny Reiza Fitriana Yuliana S Zaenal Mustofa Zahra Dinul Haq Zul Akbar