Optimization Of Posting Time And Personalization Of Machine Learning-Based Content To Increase The Engagement Rate Of Gen Z Audiences On Social Media Platforms - Nanda Ayu Frastika; Yanti Susanti; Nisma Natasha Arla (Universitas Muhammadiyah Tangerang, 2026)
Digital transformation has presented new challenges in marketing communication strategies, especially in reaching Generation Z audiences who have very dynamic content consumption characteristics. This study explores the integration of publication time optimization with content personalization using a machine learning approach to increase engagement rates on social media platforms. Through a survey of 53 Gen Z respondents who are active users of TikTok, Instagram, and YouTube, data was collected using a structured questionnaire on the Likert scale to measure eleven digital behavior variables. Descriptive statistical analysis and multiple linear regression were used to identify engagement patterns, while Random Forest and Gradient Boosting algorithms were implemented to build an optimal post-time predictive model. The findings showed that the content personalization algorithm gained a very positive reception with a score of 4.26 on a scale of 5, while posting time correlated significantly with audience engagement rates. The Random Forest model achieved 84.7% accuracy in predicting engagement patterns with an accuracy of 87.2%. The integration of the two strategies resulted in a 2.3-fold increase in interaction compared to the single approach. The research provides concrete recommendations regarding the optimal hours of content publication for each platform as well as a data-driven personalization implementation framework for user behavior that can be applied by content creators and digital marketing practitioners in designing more effective and measurable communication strategies.