Identifikasi Cuitan Stereotipe Generasi Z pada Media Sosial X menggunakan Algoritma Gated Recurrent Unit

Abstract

Stereotypes have become a prevalent social phenomenon embedded in society. According to Kamus Besar Bahasa Indonesia (KBBI), a stereotype is a conception refers to generalized assumptions about a group, often shaped by personal bias and lacking factual accuracy. Advances in information technology, especially the rise of social media, have transformed the way stereotypes are formed and spread in modern society. Social media platform X now serves as a space where stereotypes are created and reinforced. In this context, Generation Z, is one of the groups frequently targeted by various stereotypes on social media. Many of these stereotypes tend to be biased and inaccurate, ultimately affecting how society interacts with Generation Z. Such misconceptions have negative impacts on their opportunities and experiences across different aspects of life. Under these circumstances, an effective approach is needed to automate the process of identifying stereotype statements about Generation Z. This study aims to identify stereotypical tweets about Generation Z on platform X (Twitter) using the Gated Recurrent Unit (GRU) algorithm. The research uses a dataset of 3060 tweets collected through scraping methods from social media platform X. The results of this study indicate that the developed model achieved an accuracy of 88% and that the system is capable of classifying whether a tweet contains a stereotype about Generation Z or not.