Cafe Menu Selection Recommendations using the Simple Additive Weighting (SAW) Method

Joko, Sutopo (2023) Cafe Menu Selection Recommendations using the Simple Additive Weighting (SAW) Method. Journal Of Information System Research (JOSH), 05 (01). pp. 271-280. ISSN 2686-228X

Text 4. Cafe Menu Selection Recommendations using the Simple Additive Weighting (SAW) Method.pdf Download (1MB)

Abstract

In today's modern era, cafes serve a variety of food and beverage menus to their customers. However, this high diversity often leads to problems in selecting menus that suit customer preferences. The main problem faced is the difficulty for customers in choosing the menu that best suits their personal tastes and preferences, given the large number and variety of menus offered by the cafe. The purpose of this research is to find a solution to the problem of selecting a cafe menu using the Simple Additive Weighting (SAW) method. The SAW method is applied to analyze cafe menu data and provide the most suitable menu recommendations based on individual preferences. There are 6 criteria applied in this study to match customer preferences, namely price, portion size, level of popularity, quality of ingredients, compatibility with taste, and aesthetic aspects. It is hoped that this research can provide better guidance for cafe customers in choosing a menu that suits their tastes, as well as help cafe owners in increasing customer satisfaction and sales. The results of calculations using the SAW method that has been carried out get the results of 5 menus that become recommendations because they get the top rank, first Alternative A1 menu Oatmeal Protein Shake has the highest rank with a score of 0.86, second alternative A7 menu Pepperoni Pizza with a score of 0.847, third alternative A4 menu Ramen Noodle with a score of 0.813. fourth alternative A1 menu Alfredo Spaghetti with a score of 0.766 and the fifth Alternative A8 menu Seafood Fried Rice with a score of 0.738. Keywords: Recomendation; Cafe; Menu; Simple Additive Weighting (SAW)

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > Data Science
Depositing User:	ST., MT. Joko Sutopo
Date Deposited:	07 Mar 2024 05:52
Last Modified:	07 Mar 2024 05:52
URI:	http://eprints.uty.ac.id/id/eprint/15008

Actions (login required)

View Item