This research aims to design and develop a web-based information system for culinary MSME merchants to support the digitalization of promotion and business information management in a more effective and integrated manner. The existing problem is that data management and culinary information dissemination are still conducted manually, causing information related to shelters, food and beverage menus, and merchant data to be delivered less optimally to the public. The system development method used in this research is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was developed using the Laravel framework and MySQL database, while Unified Modeling Language (UML) was utilized as a system modeling tool. The system provides three access levels, namely admin, seller, and buyer, to support MSME data management, menu management, and centralized culinary information services. This study used 10 shelters as implementation testing data from approximately 120 available shelters. The testing results indicate that the system functions properly and assists users in accessing culinary information more quickly, effectively, and efficiently. The developed web-based system is expected to enhance culinary MSME promotion and support digital transformation in culinary business management.
The massive volume of textual descriptions on hotel booking platforms makes it difficult for recommendation systems to accurately match user preferences. Traditional keyword-based retrieval methods, such as TF-IDF, often struggle to capture semantic relationships when relevant terms do not explicitly overlap. This study evaluates the performance of keyword-based (TF-IDF) and embedding-based (paraphrase-multilingual-MiniLM-L12-v2) retrieval approaches in a content-based hotel recommendation system using a small-scale dataset. The dataset consists of 30 unique Traveloka hotels in Yogyakarta collected from Kaggle, representing a resource-constrained experimental setting. Evaluation was conducted using a black-box approach with 10 dynamic synthetic queries and assessed through Precision@3 (P@3) and Mean Average Precision (MAP). The results indicate that MiniLM achieved higher retrieval effectiveness than TF-IDF, with a mean P@3 of 0.3667 and a mean MAP of 0.1378, compared with 0.3000 and 0.1333, respectively. These findings suggest that embedding-based retrieval is more effective in capturing semantic information, including synonym usage and implicit contextual relationships, within the evaluated dataset. Therefore, compact embedding models such as MiniLM may provide an alternative approach to traditional keyword-based retrieval methods for small-scale recommendation systems.
Ilham Yusuf Faturochman, Aprilisa Arum Sari, Nibras Faiq Muhammad· IC-ITECHS· 0 citations
