Procedia of Social Sciences and Humanities
Vol. 3 (2022): Proceedings of the 1st SENARA 2022
Articles

An Android-Based User Recommendation System for Social Network Platform Using Collaborative Filtering Method on Youth Break the Boundaries Foundation: Sistem Rekomendasi Pengguna Aplikasi Jejaring Sosial Berbasis Android dengan Algoritma Cosine Similarity pada Youth Break the Boundaries Foundation

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  • Suhendra,
  • Yekti Asmoro Kanthi,
  • Arif Tirtana
Suhendra
STIKI Malang, Indonesia
Yekti Asmoro Kanthi
STIKI Malang, Indonesia
Arif Tirtana
STIKI Malang, Indonesia
DOI: https://doi.org/10.21070/pssh.v3i.156
Published June 1, 2022
Keywords
  • Recommender System,
  • Android,
  • Social Network,
  • Cosine Similarity
How to Cite
Suhendra, Kanthi, Y. A., & Tirtana, A. (2022). An Android-Based User Recommendation System for Social Network Platform Using Collaborative Filtering Method on Youth Break the Boundaries Foundation: Sistem Rekomendasi Pengguna Aplikasi Jejaring Sosial Berbasis Android dengan Algoritma Cosine Similarity pada Youth Break the Boundaries Foundation. Procedia of Social Sciences and Humanities, 3, 201-212. https://doi.org/10.21070/pssh.v3i.156

Copyright (c) 2022 Suhendra, Yekti Asmoro Kanthi, Arif Tirtana

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

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Abstract

The popularity of social networking services has been increasing in recent years. Hundreds of millions of active users can pose a risk of information overload. These popular services have recommender systems that can capture user interests and provide users with potentially interesting information. It is different from the relatively new Youth Break the Boundaries (YBB) social networking application, it does not yet have complete features, let alone a complex recommender system which causes user interest in using the application is low. Implementing this system on the YBB application will affect the flow of the existing system so that a comprehensive update is needed. The development uses the cosine similarity algorithm on the application search page. It is proven to be able to provide recommendations for users with an accuracy rate of 0.92. With this system, users are helped in expanding their network with other users who have the same interests.

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