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All Journal Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Surya Abdimas Building of Informatics, Technology and Science JINAV: Journal of Information and Visualization eProceedings of Engineering SmartComp Jurnal Abdimas
Indwiarti
Universitas Telkom, Bandung
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : JINAV: Journal of Information and Visualization

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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 Akbar, Muhammad Rizqi Aniq Atiqi Rohmawati Annisa Aditsania Bagas Yafitra Pandji Cici Olivia Daffa Ulayya Suhendra Dika Rizky Nurcholis Dyas Puspandari Edvan Tazul Arifin Erlangga, Samuel Erni Sumaryati Febiansyah, Muhamad Fhira Nhita Fiona Ramadhani Senduk Fitriyani Fitriyani Fitriyani Fitriyani Ika Nugraheni Irma Palupi Jondri Jondri Justinus Dedy Handyka Simanjuntak Kuntari Nur Angraini M. Abdul Jabbar May Rozakhi Takkas Mochamad Ikbal Ashari Muhammad Hafidh Raditya Ni Luh Ketut Dwi Murniati Ni Putu Winda Ardiyanti Nur, Muh. Puspandari, Dyas Putu Harry Gunawan Rafik Khairul Amin Rahmattullah, Rizky Rangga Arya Pamungkas Rian Febrian Umbara Rica Ning Nurhasanah Rizky Farida Utami Supriadi, Muhamad Rifqi Tegar Tionanda Putra Tjokorda Agung Budi Wirayuda varid vaya yusuf Vena Erla Candrika Wirayudha, Tjokorda Agung Budi Yulian Sibaroni Yuliant Sibaroni