Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method

The system is built by displaying the results of material recommendations and the results of the accuracy test of student material recommendation scores based on students' computational thinking. The processes carried out are tokenization, stopword removal, stemming, and weighting. The results of the extraction were then compared using the cosine similarity approach. The greater the cosine-similarity value produced, the more similar the two data are, so that the material recommendations will be based on the smallest cosine-similarity value between the extraction of student recommendation data. The system built has met the needs of functionality that answer the results of the problem analysis determined at the beginning of the research. This is by the results of system functionality testing carried out using black box testing. The system has been by the plan.

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Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2024-11-13
DOI
https://doi.org/10.55214/25768484.v8i6.3028
Citations
1
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
0.78
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article

Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method

Zahratul Fitri, Safriana Safriana, Nurdin Nurdin
1 citations
Edelweiss Applied Science and Technology
Educational Technology and Assessment
0.78
article

Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method

Zahratul Fitri, Safriana Safriana, Nurdin Nurdin
article en
1 citations

Abstract

The system is built by displaying the results of material recommendations and the results of the accuracy test of student material recommendation scores based on students' computational thinking. The processes carried out are tokenization, stopword removal, stemming, and weighting. The results of the extraction were then compared using the cosine similarity approach. The greater the cosine-similarity value produced, the more similar the two data are, so that the material recommendations will be based on the smallest cosine-similarity value between the extraction of student recommendation data. The system built has met the needs of functionality that answer the results of the problem analysis determined at the beginning of the research. This is by the results of system functionality testing carried out using black box testing. The system has been by the plan.

Edelweiss Applied Science and TechnologyVol. 8(6)
Malikussaleh University (ID)
Quality Education
Openalex Percentile: Top 19%
Educational Technology and Assessment
0.78
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Computational thinking-based informatics material recommendation system for vocational school students with the content-based filtering method — Zahratul Fitri, Safriana Safriana, et al. · Edelweiss Applied Science and Technology (2024) | TGRS Research Map | TGRS