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This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.

This study is dedicated to the design and implementation of a web-based inventory management platform for PT. Paramata Baraya International. replacing the manual Excel system that is ineffective and susceptible to mistakes. The Rapid Application Development (RAD) method is chosen for its quick and iterative approach to software development. The system is built using the Next.js framework and PostgreSQL as the database, with Prisma ORM for database management. Key features include item management, real-time status tracking, calibration, maintenance, equipment rental, and automatic notifications. The implementation results show a 60% increase in item search efficiency, 95% data accuracy, and an 80% reduction in manual input errors. This study is significant as a solution for companies engaged in calibration and maintenance of measuring instruments.

This research aims to develop the company website PT. Rantangin Digital Indonesia uses the latest technology, namely Next.js and Tailwind CSS. This website development was carried out to increase visibility and user experience in accessing the services offered by the company. Next.js was chosen as the development framework because of its ability to build fast, scalable and SEO-friendly web applications, while Tailwind CSS was used to speed up the interface design process with a flexible and responsive utility-first approach. The development method used is the Waterfall method, which consists of planning, design, implementation, testing and maintenance stages. The development results show that the website built has succeeded in fulfilling the company's goals of providing a digital platform that is easy to access, responsive on various devices, and has optimal performance. By using Next.js and Tailwind CSS, website development can be done efficiently and effectively, and provide a better user experience. Recommendations for further development include integration with content management systems and analytical features for monitoring website performance.