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Journal : JINAV: Journal of Information and Visualization

Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking Vena Erla Candrika; Jondri Jondri; Indwiarti Indwiarti
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1518

Abstract

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.
Retweet Prediction Using Artificial Neural Network Method Optimized with Firefly Algorithm Supriadi, Muhamad Rifqi; Jondri, Jondri; Indwiarti, Indwiarti
JINAV: Journal of Information and Visualization Vol. 4 No. 2 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1903

Abstract

Twitter is one of the social media platforms that has a large user base across various demographics. Users can use Twitter to search for information about celebrities, political issues, products, and trending topics of discussion. The information shared on Twitter can be referred to as tweets. Tweets can be further shared by other users using the retweet feature, which allows the tweet to reach a wider audience. This research aims to build a retweet prediction system and examine how tweets will spread. The method used in this research is Artificial Neural Network classification optimized with Firefly Algorithm, based on user-based and content-based features. This modeling approach demonstrated the best results after applying imbalanced class handling using oversampling with the SMOTE technique. The F1-Score obtained in this research is 88.07%.
Co-Authors Achmad Hussein Sundawa Kartamihardja Achmad Rizal Achmad Salim Aiman Aditya Kusuma Setyanegara Adnan Hassal Falah Ahmad, Fathih Adawi Akbar, Muhammad Rizqi Al Azhar Al Azhar Alfredo Alfredo Ali Zainal Abidin Assajjad Anditya Arifianto Andrian Yoga Pratama Anggit Nourislam Anggit Nourislam Anggit Nourislam Aniq Atiqi Rohmawati Anisa Nur Aini Annisa Aditsania Arief Hutauruk Arifudin Achmad Artamira Rizqy Amartya Maden Arya Rafif Muhammad Fikri Astri Asroviana Putri Aswindo Putra Bambang Ari Wahyudi Bayu Prabawa Bintang Aryo Dharmawan Bramandyo Widyarto, Edgarsa Daffa Ulayya Suhendra Danang Triantoro M Danang Triantoro Murdiansyah Danu Ardiyanto Dea Taradipa Ardiagarianti Dede Tarwidi Deni Saepudin Denny Maulana Deny Sugiarto Wiradikusuma Devy Yendriani Dieka Nugraha Karyana Ditta Febriany Sutrisna Diwan Mukti Pambuko Diwan Mukti Pambuko, Diwan Mukti Dyas Puspandari E Handayani Echa Pangersa Sugianto Oeoen Edvan Tazul Arifin Eka Handayani Eka Handayani Ema Rachmawati Emha Ainun Erlina Febriani Ersa Christian Prakoso Fahrudin Julianto Faisal HAmdani Fakhrana Kurnia Sutrisno Fani Nuraini Farisi, Kamaludin Hanif Fauzan Azhim Umsohi Fazlur Rahman Amri Febiansyah, Muhamad Fery Kun Widi Yudantyo Firdaniza Firdaniza Fitriyani Fitriyani Fransisca Arvevia Intan Angelia Ghina Khoerunnisa Giali Ghazali Guntur Virgenius Hadi, Salman Farisi Setya Hafidz Firmansyah Hafidz Firmansyah Hafiz Denasputra Halprin Abhirawa Hendra Prasetyanwar Huda Sepriandi Ibrahim Husna Aydadenta Ida Bagus Gde Narinda Giriputra Ika Puspita Dewi Ilham Muhammad Iman Nur Fakhri Imannda Kusuma Putra Indwiarti indwiarti Iqbal Dwihanandrio Irgi Aditya Rachman Irma Palupi Irwan Ramadhana Kamaludin Hanif Farisi Karina Priscilia Karina Priscilia Kemas Muslim Lhaksmana Kukuh Rahingga Permadi Kurniawan Nur Ramadhani Ledya Novamizanti Mahmud Dwi Sulistiyo Mahmud Sulistiyo Megi Rahma Dony Moch. Bijaksana Muh. Arfan Arsyad Muhalani, Raisul Muhamad Febiansyah Muhammad Farhan Muzakki Muhammad Fikrie Abdillah Muhammad Ghazali Suwardi Muhammad Hasan Muhammad Hasbi Ashshiddieqy Muhammad Irfan Fathurrahman Muhammad Wildan Putra Aldi Muslim Lhaksmana, Kemas Naufal Dzaky Anwari Naufal Furqan Hardifa Nurseno Bayu Aji Nurseno Bayu Aji Patma Oktaviana Puspandari, Dyas Putri Haryati Rizki Putri Haryati Rizki Putu Harry Gunawan Rafi Hafizhni Anggia Rahadian, Muhammad Rafi Raisul Muhalani Ratih Puspita Furi Redha Arifan Juanda Redi Nurjamin Renette Ersti Reza Harun Rian F. Umbara Rian F. Umbara, Rian F. Rian Febrian Umbara Rica Ning Nurhasanah Rini Shintawati Rita Rismala Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky, Fariz Muhammad Roizal Manullang Siti Sa'adah Siti Saadah Sugondo Hadiyoso Supriadi, Muhamad Rifqi Syadzily , Muhammad Hasan Syifa Khairunnisa Salsabila Tedy Suwega Theo Andrew Tiara Laksmi Basuki Tifani Intan Solihati Tjokorda Agung Budi Wirayuda Ulky Parulian Wibowo Untari Novia Wisesty Untari Wisesty Varian Vianandha Vena Erla Candrika Vera Suryani Widi Astuti Widi Astuti Yahya Setiawan Yosua Marchel