| dc.description.abstract | Since the Covid-19 pandemic started, many people have been advised to take
supplements or vitamins to boost their immune systems. There are so many different
types of vitamins and supplements available that it often confuses people when they
try to choose the right products for their health. In the current digital era, all
information can be accessed easily via online platforms and one of the popular
online platforms that provides a lot of information about vitamins and supplements
is halodoc.com. However, with all this information out there, users might feel
overwhelmed trying to find products that fit their needs. That's why we need an
effective recommendation system to help users find the best vitamin and supplement
products for their health and immune needs. One of the recommendation system
approaches that will be used in this research is the Content-Based Filtering
method. This method focuses on product characteristics, specifically the
composition of vitamins and supplements. This research uses uses a word weighting
method known as the Term Frequency-Inverse Document Frequency (TF-IDF)
algorithm and a similarity level calculation method called the Cosine Similarity
algorithm. The result of this research is a recommendation for the product
"Becefort," specifically the product "Bexicom," which has a Cosine Similarity value
of 0.8340. The recommendation system that has been developed is deployed on a
website for general access. With this recommendation system in place, it is hoped
that it will make it easier and save time for consumers in selecting replacement
vitamins and supplements that match the composition or content needed by the
consumers. | en_US |
