The development of digital technology has brought significant changes in the invitation industry, replacing conventional paper invitations with digital invitations that are more environmentally friendly and efficient. This study aims to analyze the business model of the Untukmu digital invitation business using the Business Model Canvas (BMC) framework. BMC includes nine key elements, namely customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partners, and cost structures. The results of the analysis show that the personalization approach, interactive features such as RSVP and photo galleries, and digital promotion strategies through social media are the main competitive advantages of this business. This study also identifies the challenges faced, such as market competition and limitations of UI design, and provides strategic recommendations to overcome these obstacles. With this analysis, it is hoped that the Untukmu digital invitation business can increase its competitiveness and business sustainability.

The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques such as grayscale, Gaussian blur, and adaptive threshold to identify the parking slot status, whether empty or occupied, with good accuracy. The parking slot coordinate data is stored in CSV format to ensure efficient data management. Experimental results with video recordings show that the system is able to operate well in various parking conditions. The system proved to be cost-effective and easy to implement, making it an ideal solution for parking managers who want to improve management efficiency without being burdened with high costs. This research offers a practical solution to help motorists and parking managers optimize parking space usage, reduce search time, and minimize negative impacts such as congestion and carbon emissions.