* Corresponding Author
Hide
Show all
Collaborative Filtering Recommender System pada Virtual 3D Kelas Cendekia
https://doi.org/10.22146/ijeis.28729 Angga Setia Wardana(1*), Muhammad Idham Ananta Timur(2)
(1) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta, Indonesia
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Intelligent Clasrooms is a concept of modern learning process where users can perform collaborative learning wherever and whenever. With learning in Intelligent Classroom, users can get different learning experience where learning process is expected to run more effectively and efficiently. One application of the Intelligent Classrooms concept is learning by utilizing the virtual world. The information collected in the Intelligent Classroom will increase so that a system is needed. The recommendation system of collaborative filtering is the most appropriate system with the intellectual class. With the sparsity of training rate of 80%, it is implemented a collaborative filtering recommendation system with error rate which if calculated with RMSE is 1.060709 or it can be said that the accuracy level is 78.79%.
Keywords
Full Text:
PDFReferences
[1] R. J. Amineh and H. D. Asl, “Review of constructivism and social constructivism,” J. Soc. Sci. Lit. Lang., vol. 1, no. 1, pp. 9–16, 2015.
available: http://www.blue-ap.org/j/List/4/iss/volume%201%20(2015)/issue%2001/2.pdf
[2] B. K. Pagano and T. Crosby, “Making Good on the Promise of Immersive,” no. january, pp. 45–47, 2017.
available: http://ieeexplore.ieee.org/document/7786974/
[3] G. Preethi and P. V. Krishna, “Application of Deep Learning to Sentiment Analysis for Recommender System on Cloud,” 2017.
available: http://ieeexplore.ieee.org/document/8035341/
[4] S. Yan, K. Lin, X. Zheng, and S. Member, “An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks,” vol. 29, no. 10, pp. 2086–2099, 2017.
http://ieeexplore.ieee.org/document/7954736/
[5] D. Margaris, “Improving Collaborative Filtering ’ s Rating Prediction Quality in Dense Datasets , by Pruning Old Ratings,” 2017.
available: http://ieeexplore.ieee.org/document/8024683/
[6] W. Jiang and Liping Yang, “Research of Improved Recommendation Algorithm Based on Collaborative Filtering and Content Prediction,” no. Iccse, pp. 598–602, 2016.
available: http://ieeexplore.ieee.org/document/7581648/
[7] R. Sharma, D. Gopalani, and Y. Meena, “Collaborative Filtering – Based Recommender System : Approaches and Research Challenges,” pp. 1–6, 2017.
available: http://ieeexplore.ieee.org/document/7977363/
[8] B. M. Arsandi, T. W. Widodo, and F. Faizah, “Purwarupa Sistem Pembuka Pintu Cerdas Menggunakan Perceptron Berdasarkan Prediksi Kedatangan Pemilik,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 7, no. 1, p. 83, Apr. 2017 [Online]. Available: https://jurnal.ugm.ac.id/ijeis/article/view/16840. [Accessed: 29-Aug-2017]
[9] M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, “Collaborative Filtering Recommender Systems,” Found. Trends® Human–Computer Interact., vol. 4, no. 2, pp. 81–173, 2010.
available: https://dl.acm.org/citation.cfm?id=2185828
[10] A. A. S. Gunawan and D. Suhartono, “Developing Recommender Systems for Personalized Email with Big Data,” pp. 77–82, 2016.
available: http://ieeexplore.ieee.org/document/7872893/
DOI: https://doi.org/10.22146/ijeis.28729
Article Metrics
Abstract views : 6403
| 
views : 4519
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1