Version 1
: Received: 22 June 2023 / Approved: 23 June 2023 / Online: 23 June 2023 (11:59:37 CEST)
How to cite:
Sukestiyarno, Y.L.; Sapolo, H.A.; Sofyan, H. Application of Recommendation System on E-Learning Platform Using Content-Based Filtering with Jaccard Similarity and Cosine Similarity Algorithms. Preprints2023, 2023061672. https://doi.org/10.20944/preprints202306.1672.v1
Sukestiyarno, Y.L.; Sapolo, H.A.; Sofyan, H. Application of Recommendation System on E-Learning Platform Using Content-Based Filtering with Jaccard Similarity and Cosine Similarity Algorithms. Preprints 2023, 2023061672. https://doi.org/10.20944/preprints202306.1672.v1
Sukestiyarno, Y.L.; Sapolo, H.A.; Sofyan, H. Application of Recommendation System on E-Learning Platform Using Content-Based Filtering with Jaccard Similarity and Cosine Similarity Algorithms. Preprints2023, 2023061672. https://doi.org/10.20944/preprints202306.1672.v1
APA Style
Sukestiyarno, Y.L., Sapolo, H.A., & Sofyan, H. (2023). Application of Recommendation System on E-Learning Platform Using Content-Based Filtering with Jaccard Similarity and Cosine Similarity Algorithms. Preprints. https://doi.org/10.20944/preprints202306.1672.v1
Chicago/Turabian Style
Sukestiyarno, Y.L., Hasballah Askar Sapolo and Hizir Sofyan. 2023 "Application of Recommendation System on E-Learning Platform Using Content-Based Filtering with Jaccard Similarity and Cosine Similarity Algorithms" Preprints. https://doi.org/10.20944/preprints202306.1672.v1
Abstract
This study aims to apply a Recommendation System with Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms on the E-Learning Platform. Recommendation systems deal with how to provide personalized recommendations to users efficiently. The Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms can be used to calculate the similarity value between E-Courses on the E-Learning Platform. Implementation for Recommendation System using Google Colaboratory with Python programming language. In the application of the Recommendation System dataset, Coursera Free Dataset consists of 975 instances. The recommendation results use the Jaccard Similarity algorithm with an average similarity value of 0.3 while the value of Cosine Similarity with the average similarity value is 0.6 where the similarity value of Cosine Similarity is higher. Based on the results of the Mean Absolute Error in the low recommendation system, the average MAE value for all iterations of Jaccard Similarity algorithm is 0.013 and for the Cosine Similarity algorithm the average MAE value for all iterations is 0.014. This shows that the Recommendation System with Jaccard Similarity and Cosine Similarity algorithms can be used on the E-Learning Platform to provide efficiency solutions for personalized recommendations.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
