Marketplace Seller Recommender with User-Based Multi Criteria Decision Making

摘要

This research develops a recommender system for seller selection of several online marketplace in Indonesia. Since users are provided with too many merchants with various offers regarding a product, it then creates such difficulty and longer time for them to decide which merchant they should choose to get the efficient effort but optimum results. Case studies in several of the most popular online shops were adopted: Tokopedia, Shopee and Bukalapak., where respondents make online transaction the most. Criteria stated are product price, seller location, seller reputation, the number of sold products and expedition support. This study applied a method of Fuzzy Simple Additive Weighting to normalize by using weight authorized from user preference. The experiment applied a user-based method of testing due to each preference and method to place the rankings. At the end, a merchant with the highest point placed as the top rank and displayed as the recommendation.