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Nur Auliya Saleh, Muhammad Daivany
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Computer Science & IT

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Sistem Rekomendasi Pemilihan Produk Menu Makanan Pada Katering Manshurin Menggunakan Metode Knowledge Based Recommendation Nur Auliya Saleh, Muhammad Daivany; Joni Maulindar; Bondan Wahyu Pamekas
Jurnal ICT: Information Communication & Technology Vol. 24 No. 1 (2024): JICT-IKMI, Juli, 2024
Publisher : LPPM STMIK IKMI Cirebon

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Abstract

Current technological developments have become an important necessity for human life. The use of information technology cannot be denied in streamlining operations in various sectors, including in the culinary business world. Manshurin Catering is a business that operates in the food service sector. Currently, Manshurin Catering does not yet have a system or service platform in the form of menu recommendation information, so customers have to come to the location to order and consult about a menu that suits their tastes, so many customers have difficulty deciding on a food menu. This research aims to build a knowledge based recommendation system with a cased based approach which is hoped to help customers choose the right food menu according to their needs. The research method used involves data collection through library studies, interviews and observation. In developing this recommendation system using the RAD (Rapid Application Development) method by going through several stages including requirements design, system design, development and implementation. This research uses 10 samples of food menu data using 5 attributes, namely type of food, price, food ingredients, course menu, and rating, where each menu has a weight of 20% or 0.2 which will be calculated by the similarity value. to determine the comparison between customer demand and available food products. The results of this research are after calculating the data using the knowledge based recommendation method for food menu products that are similar to customer requests, namely the fried chicken rice menu with a similarity value of 0.8 which will be displayed by the system as a recommendation menu.
Co-Authors Maulindar, Joni Pamekas, Bondan Wahyu