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.

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.

Abstract. This study presents a web-based system for identifying traditional herbal leaves using K-Nearest Neighbors (KNN) and image processing techniques focused on analyzing leaf shape and color. The dataset used consists of images of various types of herbal leaves, providing a basis for classification and medicinal benefit information retrieval. The system was tested with multiple leaf samples to assess accuracy, speed, and effectiveness in identifying leaf types based on visual characteristics. Results show that the system can recognize different types of herbal leaves and display information on their medicinal properties in a user-friendly interface..