Application of Extreme Programming Methods in the Design and Building of the Nusantara Capital Sentiment Analysis System

Information about the capital city of the archipelago (IKN) in the digital era serves as a platform for individuals to express views on development, policies, and socio-economic impacts. Such information often contains personal emotional expressions, categorized as negative, neutral, or positive sentiments. This study aims to design a sentiment analysis system to evaluate public opinions regarding IKN. The system utilizes Google NLP services, which offer sentiment measurement features for analyzed text, and web scraping techniques to automate data collection from online sources. The development process employs the Laravel framework and follows the Extreme Programming approach, which ensures work efficiency. Sentiment analysis is conducted using the Support Vector Machine (SVM) method, achieving an accuracy rate of 95%. The system is designed to be web-based, ensuring accessibility across devices, including smartphones and computers. The results demonstrate that this sentiment analysis system can help individuals, organizations, and governments gain deeper insights into public perspectives on IKN. Furthermore, it serves as a valuable tool for strategic decision-making and policy evaluation related to IKN development. Future research may explore expanding the data sources and integrating more advanced analytical techniques to improve system performance.