Penerapan Metode Collaborative Filtering Untuk Personalized Learning Content Pada Learning Management System (LMS)
DOI:
https://doi.org/10.30865/jurikom.v9i2.3887Keywords:
Collaborative Filtering, Implicit Feedback, MAE, Personalization, UAT, SparsityAbstract
The delivery of appropriate learning content can be one of the factors that can increase satisfaction, motivation, and interest of learners during learning sessions. But on the other hand, due to a large amount of learning content available on LMS (Learning Management System) and the difficulty of determining learning content that suits the needs and interests of each learner, it often causes some learning content to be overlooked by them. Therefore, this paper aims to design a personalized learning content system in LMS for learners. The main objective of this study is to provide learning content suggestions or recommendations for learners, based on the course module they previously accessed by applying Collaborative Filtering method. This method is used by utilizing dataset in the form of implicit feedback, obtained from the activities of learners when interacting with LMS. The UAT (User Acceptance Test) results show that the personalization system has been well received by as many as 82.67% of learners based on three aspects, those are interface, user, and system interaction aspects. Moreover, the MAE (Mean Absolute Error) calculation shows that this system has the best accuracy rate at a 10% sparsity level with the lowest average value of 0.4514.
References
Chourishi D, Buttan C K, Chaurasia A, dan Soni A. 2015. Effective E-Learning Through Moodle. India. International Journal of Advance Technology & Engineering Research (IJATER).
Imran H, Belghis-Zadeh M, Chang T W, Kinshuk, dan Graf S. 2016. PLORS: A Personalized Learning Object Recommender System. Vietnam. Springer Vietnam J Comput Sci.
R. K. Ellis. 2009. Field Guide to Learning Management Systems. ASTD Learn. Circuits.
Tjong Y, Sugandi L, Nurshafita A, Magdalena Y, Evelyn C, dan Yosieto N S. 2018. User Satisfaction Factors on Learning Management System Usage. Indonesia. IEEE.
Hidayat A F, Suwawi D D J, dan Laksitowening K A. 2020. Learning Content Recommendation on Personalized Learning Environment Using Collaborative Filtering Method. Indonesia. IEEE.
Guabassi I E, Bousalem Z, Achhab M A, Jellouli I, dan Mohajir B E E. 2018. Personalized Adaptive Content System for Context-aware Ubiquitous Learning. Morocco. Science Direct Procedia Computer Science.
Montgomery A L, dan Smith M D. 2009. Prospect for Personalization on the Internet. Pittsburgh. Science Direct Journal of Interactive Marketing.
Handrico, A. 2012. Sistem Rekomendasi Buku Perpustakaan Fakultas Sains dan Teknologi dengan Metode Collaborative Fitlering. Pekanbaru. Universitas Islam Negeri Sultan Syarif Kasim Riau.
Zhao Z D, dan Shang M S. 2010. User-based Collaborative-Filtering Recommendation Algorithms on Hadoop. China. IEEE.
Gulla J A. 2014. Collaborative Filtering in the News Domain with Explicit and Implicit Feedback. Norwegia. Norwegian University of Science and Technology Chapter 4 Page 22-23 and Chapter 11 Page 72.
Claypool M, Le P, Wased M, dan Brown D. 2001. Implicit Interest Indicators. USA. Computer Science Department.
Moon J, Kum S W, dan Lee S. 2017. Introduction to the Design of Personalized User Interface Platform with Recommended Contents. Japan. ICCP.
Tarus J, Niu Z, dan Khadidja B. 2017. E-Learning Recommender System Based on Collaborative Filtering and Ontology. China. International Scholarly and Scientific Research & Innovation.
Sarwar B, Karypis G, Konstan J, dan Riedl J. 2001. Item-Based Collaborative Filtering Recommendation Algorithms. Hong Kong. ACM.
Agustina R dan Suprianto D. 2018. Analisis Hasil Pemanfaatan Media Pembelajaran Interaktif Aljabar Logika dengan User Acceptance Test (UAT). Malang. SMATIKA Jurnal Vol 08 No 02.
Yusup F. 2018. Uji Validitas dan Reliabilitas Instrumen Penelitian Kuantitatif. Banjarmasin. Jurnal Tarbiyah: Jurnal Ilmiah Kependidikan.
