Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

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

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 3 (2021) · pp. 265–274

DOI
10.22146/ijccs.65623
ISSN
2460-7258, 1978-1520
Published
2021-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Collaboration network of the authors

28 authors · 46 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Politeknik Negeri Ketapang
  • National Central University
  • Muria Kudus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada4
Anny Kartika SariUniversitas Gadjah Mada3
Sri T. HartatiUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Fajar Ibnu TufeilAffiliation unknown1
Mujiono MujionoAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Ria AstriratmaUniversitas Gadjah Mada1
Agus HarjokoUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Damar RiyadiUniversitas Gadjah Mada1
Ilham UnggaraUniversitas Gadjah Mada1
Yessi YunitasariUniversitas Gadjah Mada1
Dias Aziz PramuditaUniversitas Gadjah Mada1
Abidah ElcholiqiUniversitas Gadjah Mada1
Wahyu SrimulyaniUniversitas Gadjah Mada1
Hans Satria KusumaUniversitas Gadjah Mada1
Yunita SariUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Muhammad Bintang BahyUniversitas Gadjah Mada1
Muhammad Fawwaz MaydaUniversitas Gadjah Mada1
Aditya PermanaUniversitas Gadjah Mada1
Timothy K. ShihNational Central University1
Anastasya LatubessyMuria Kudus University1
Sri KusrohmaniahUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Online Learning Video Recommendation System Based on Course and Sylabus Using Content-Based Filtering</dc:title>
	<dc:creator>Ramadhan, Faisal</dc:creator>
	<dc:creator>Musdholifah, Aina</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">recommendation system, learning videos, content-based filtering, cosine similarity</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/65623</dc:identifier>
	<dc:identifier>10.22146/ijccs.65623</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 3 (2021): July; 265-274</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/65623/31840</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>