<![CDATA[Advances in information technology and the increasing use of social media have significantly influenced the behavior of Generation Z. The generation born between 1997 and 2012 is known to be very familiar with the digital world, but also faces challenges such as lack of in-person social interaction and the risk of mental health disorders. This study aims to identify and classify public sentiment towards Generation Z on social media, especially on platform X (formerly Twitter). The method used is the Support Vector Machine (SVM). This research was carried out through several stages, namely the collection of 1607 data in the form of text using crawling techniques, pre-processing of text (tokenization, case folding, removal of stopwords, stemming, and normalization), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data is then classified into three sentiment categories: positive, negative, and neutral using SVM. Evaluation was carried out by measuring accuracy, recall value, and F1-score value through a confusion matrix. The results showed that the measurement of an accuracy value of 85%, a precision value of 85%, a value of recall of 95% and an F1-score value of 90% that SVM was able to classify sentiment with high accuracy and stability. In addition, SVM has been shown to be more effective than other methods studied in previous studies. The data analyzed shows that most sentiment towards Generation Z is negative, reflecting public concern about the behavior and mindset of this generation. This research is expected to be a reference for academics, practitioners, and policymakers in understanding public opinion and designing targeted policies for the younger generation. Keywords: Sentiment Analysis, Generation Z, Support Vector Machine, Social Media, Machine Learning.]]>
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