<![CDATA[The swift expansion of social media utilization has positioned Generation Z among the primary demographics in acquiring and disseminating information. The substantial frequency of social media engagement along with the standard of information obtained impacts information-sharing behaviour, rendering a classification model essential for determining behavioural patterns. This research seeks to apply the Decision Tree algorithm to classify the information-sharing behaviour of Generation Z according to the variables of Information Quality (X1) and Social Media Usage Intensity (X2), as well as to deploy the classification model within a web-based system. The inquiry utilized a quantitative methodology adopting the CRISP-DM framework, comprising Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The empirical data were gathered via questionnaires administered to 113 Generation Z participants actively engaging with social media across Semarang Regency. The classification model was constructed utilizing RapidMiner, whereas its effectiveness was assessed employing a Confusion Matrix. The findings demonstrated that the Decision Tree algorithm attained an accuracy rate of 82.61% and produced 13 decision rules capable of classifying information-sharing behaviour into three tiers: high, moderate, and low. The constructed model was effectively integrated into a web-based application built with PHP and MySQL, allowing the classification procedure to operate automatically according to user entries. These outcomes reveal that the Decision Tree algorithm not merely delivers a classification model with robust performance, but also admits effective integration into a web-based system to assist the classification of Generation Z's information-sharing behaviour.]]>
