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The development of Artificial Intelligence (AI) technology has significantly changed strategy and decision making in marketing management. However, the massive use of AI raises new challenges regarding ethics, transparency and governance. This research aims to analyze the impact of using AI, especially recommender systems and large language models (LLMs), on the effectiveness of marketing decisions, as well as the role of AI governance in controlling emerging ethical issues. The research method uses a quantitative approach with Structural Equation Modeling (SEM) analysis of data collected from 250 marketing professionals in Indonesia. The research results show that the use of AI has a significant positive effect on the effectiveness of marketing decisions (β=0.62, p<0.001), but also raises ethical issues (β=0.48, p<0.01). Ethical issues were proven to reduce the effectiveness of marketing decisions (β=-0.31, p<0.05), while good AI governance was able to moderate the negative impact of ethical issues (β=0.27, p<0.05). These findings underscore the importance of AI governance in building effective and ethical marketing systems.

Language development is one of the main pillars in early childhood growth. Strong language skills are not just about being able to speak, but also include the ability to listen, understand, and use language to think and interact. This ability becomes an important foundation for children's future success, both in formal education and social life. In the early childhood stage, the role of teachers in kindergartens (TK) is very crucial. Teachers are the child's first social environment outside the home, and the interactions they have every day will greatly influence the child's language development. This research will thoroughly explore the various strategic roles played by teachers in stimulating children's language development, and how these roles can be effectively applied in the classroom environment.

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.