Clothing Recommendation System Using the K-Nearest Neighbor Method

Authors

  • Arya Maghrizal Putra Telkom University, Indonesia
  • Muhamad Irsan Telkom University, Indonesia
  • Muhammad Faris Fathoni Telkom University, Indonesia

DOI:

10.33395/sinkron.v8i2.13377

Keywords:

Clothing Recommendation System, K-nearest Neighbor, Classification, Accuracy, System Performance

Abstract

The world of fashion and the way we interact with it has been transformed by advances in information and communication technology. Clothing recommendation applications have become increasingly common, helping people choose clothes that suit their style and preferences. This study suggests using the KNN Method as a basis for building a more intelligent and personalized clothing recommendation system. To address the growing need for accurate clothing recommendations that match users' preferences, The goal of this research is to create a clothing recommendation system that can help users choose more appropriately because advances in technology have made it possible to gather and examine user data more thoroughly. In this study, the clothing recommendation system was implemented using the KNN Method. We ran simulations by setting the clothing dataset's parameter K value from 3 to 11. The simulation results show that the system's performance reaches its peak at parameter value K=8. We measured the system's accuracy, precision, and recall at this K value in order to assess its performance. The results show that the clothing recommendation system uses the KNN Method. A clothing recommendation system based on the KNN Method with the parameter K=8 has proven successful in classifying clothes with an accuracy of 83,67%.

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How to Cite

Putra, A. M. ., Irsan, M. ., & Fathoni, M. F. . (2024). Clothing Recommendation System Using the K-Nearest Neighbor Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 797-804. https://doi.org/10.33395/sinkron.v8i2.13377

Issue

Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024

Section

Articles

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Copyright (c) 2024 Arya Maghrizal Putra, Muhamad Irsan, Muhammad Faris Fathoni

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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