Publication Search

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.

PT. Pelayaran Nasional Indonesia (PELNI) is a company engaged in the field of marine transportation services, namely passenger ships, roro ships, container ships, pioneer ships and so on. Therefore, the importance of "service" to customer satisfaction (passengers) is very influential. Customer service can be defined as all forms of customer service in the form of services and in the form of goods which in principle are the responsibility and are carried out by PT. Pelayaran Nasional Indonesia (PELNI) Medan Branch, in order to fulfill the needs of the community and comply with regulations and laws.