Decision Support System Selecting The Best Skincare Using The Oreste Method

Abstract

The development of the beauty industry, especially in skincare products, raises the need for a system that helps individuals choose skincare products according to their needs and skin conditions. This research aims to develop a Decision Support System (SDM) using the ORESTE Method to facilitate the selection of the best skincare. ORESTE is a multi-criteria decision-making method capable of dealing with uncertainty and ambiguity. This research adapts the ORESTE Method in the context of skincare product selection by considering important criteria such as price, quality, brand, age, and suitability to the user's skin type. The sampling technique that will be used in this research is non probability sampling technique with purposive sampling type.  Applied research is the type of research used in this study. As a continuation of basic research, applied research aims to solve a problem in society, industry, or government. The type of data used is primary data, and the data source used is a questionnaire / questionnaire. The data was used to develop a decision model using the ORESTE Method, which was then tested and validated using a diverse sample of skincare products. The results show that the developed SPK provides skincare product recommendations according to user preferences and needs. By using the ORESTE Method, this SPK is able to overcome the complexity in skincare decision making, providing accountable results. This research contributes to making it easier for individuals to choose skincare products according to their skin conditions and preferences, and serves as a basis for the development of similar systems in the beauty field. An attempt to show the novelty between this research and previous research was made by using the ORESTE method in skincare product selection. The results show MS Glow as the best alternative with a preference value of 1.348, followed by Wardah, Scarlett, Garnier, and Pond's with their respective preference values.