Online Learning Video Recommendation System Based on Course and Sylabus Using Content-Based Filtering

https://doi.org/10.22146/ijccs.65623

Faisal Ramadhan(1*), Aina Musdholifah(2)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Learning using video media such as watching videos on YouTube is an alternative method of learning that is often used. However, there are so many learning videos available that finding videos with the right content is difficult and time-consuming. Therefore, this study builds a recommendation system that can recommend videos based on courses and syllabus. The recommendation system works by looking for similarity between courses and syllabus with video annotations using the cosine similarity method. The video annotation is the title and description of the video captured in real-time from YouTube using the YouTube API. This recommendation system will produce recommendations in the form of five videos based on the selected courses and syllabus. The test results show that the average performance percentage is 81.13% in achieving the recommendation system goals, namely relevance, novelty, serendipity and increasing recommendation diversity.

Keywords


recommendation system, learning videos, content-based filtering, cosine similarity

Full Text:

PDF


References

Ammy, P. M. and Wahyuni, S., 2020, Analisis Motivasi Belajar Mahasiswa Menggunakan Video Pembelajaran Sebagai Alternatif Pembelajaran Jarak Jauh (PJJ), Jurnal Mathematic Paedagogic, 5(1), 27-35.

Persson, A.C., Fyrenius, A. and Bergdahl, B., 2010, Perspectives on using multimedia scenarios in a PBL medical curriculum, Medical teacher, 32(9), pp.766-772.

Van Den Hurk, M. M., Wolfhagen, I. H., Dolmans, D. H., and Van Der Vleuten, C. P. (1999), The impact of student‐generated learning issues on individual study time and academic achievement, Medical Education, 33(11), 808-814.

Burke, R., 2002, Hybrid recommender systems: Survey and experiments, User modeling and user-adapted interaction, 12(4), pp.331-370.

Adam, N. L., Sulaiman, M. S. A., and Soh, S. C., 2019, Calculus video recommender system, Journal of Physics: Conference Series, 1366, 1-8.

Uysal, A.K. and Gunal, S., 2014, The impact of preprocessing on text classification, Information Processing & Management, 50(1), pp.104-112.

Srividhya, V. and Anitha, R., 2010, Evaluating preprocessing techniques in text categorization, International journal of computer science and application, 47(11), pp.49-51.

Khusro, S., Ali, Z. and Ullah, I., 2016. Recommender systems: issues, challenges, and research opportunities. In Information Science and Applications (ICISA) 2016 (pp. 1179-1189). Springer, Singapore.

Rahman, A., Wiranto, W. and Doewes, A., 2017. Online news classification using multinomial naive bayes. ITSMART: Jurnal Teknologi dan Informasi, 6(1), pp.32-38.

Nurjannah, M., Hamdani, H., and Astuti, I. F., 2016, Penerapan Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) untuk Text Mining, Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 8(3), 110-113.

Munot, N. and Govilkar, S.S., 2014, Comparative study of text summarization methods, International Journal of Computer Applications, 102(12).



DOI: https://doi.org/10.22146/ijccs.65623

Article Metrics

Abstract views : 4065 | views : 3408

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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


Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs


View My Stats1
View My Stats2