Implementasi dan Analisis Model Machine Learning Decision Tree untuk Deteksi Akun Palsu di Twitter

 Risqa Taufik (Universitas Budi Luhur, Jakarta, Indonesia)
 Risti Jimah (Universitas Budi Luhur, Jakarta, Indonesia)
 (*)Achmad Solichin Mail (Universitas Budi Luhur, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: March 12, 2024; Published: April 23, 2024

Abstract

In this digital era, social media platforms have become an integral part of daily life, facilitating social interaction, information exchange, and participation in public discussions. However, the emergence of bot or fake accounts on social media, especially Twitter, has posed a new challenge. These accounts are often used to disseminate inaccurate or misleading information, which can negatively impact social and political dynamics. This research focuses on the problem of detecting fake accounts on Twitter. We conducted an in-depth analysis of user profiles and their posting behavior. We collected data from various Twitter accounts, both real and fake, and categorized them for model creation. The Decision Tree model is used in this research for fake account detection. We chose this method because of its effectiveness in identifying and distinguishing between real and fake accounts based on predetermined features. The model creation process involves training and testing the model with the categorized dataset. As a case study, we applied this model to the followers of the Twitter account of Budi Luhur University. The result is, this model is able to identify fake accounts with an accuracy rate reaching 99%. This shows that our approach in using the Decision Tree Model is effective in dealing with the problem of detecting fake accounts on Twitter.

Keywords


Fake Accounts (Bots); Account Detection; Decision Tree Classification; Machine Learning; Twitter Platform

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