Content-Based Filtering Laptop Recommendation System at Els Computer Shop Semarang
DOI:
https://doi.org/10.33379/gtech.v8i1.3490Keywords:
recommendation system, Content-based filtering, Encoding data, Cosine similarityAbstract
A recommendation system is a software technique that provides suggestions or
recommendations based on user preferences. This paper examines a
recommendation system for laptops at Els Computer Shop in Semarang, using
a content-based filtering approach to help customers select laptops that match
the attributes they are looking for. The prototype method was chosen as the
system development technique because it facilitates interaction between system
developers and can address issues between users and analysts. The design of this
recommendation system involves two types of data encoding: one-hot encoding
and ordinal encoding. One-hot encoding transforms categorical laptop data into
binary numbers (0 or 1), while ordinal encoding converts categorical laptop
data based on a specific order into numeric values (0, 1, 2, 3). The transformed
data is then calculated using cosine similarity to determine the similarity score
of recommended laptops. The results of the laptop recommendation system
display three laptops that are most similar to the searched laptop index based
on cosine similarity calculations.
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