Recommendation system for selecting web programming learning materials for vocational high school students using multi-criteria recommendation systems

Wahyuliningtyas, Lia, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219 and Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762 (2024) Recommendation system for selecting web programming learning materials for vocational high school students using multi-criteria recommendation systems. International Journal of Advances in Data and Information Systems, 5 (1). pp. 49-61. ISSN 27213056

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Abstract

In the independent curriculum, the learning that is carried out focuses on developing character, student competence and honing interests, talents. So the amount of learning material given to students does not have to be complete or less. Apart from that, the independent curriculum no longer burdens students with achieving a minimum score because assessments no longer use Minimum Completeness Criteria (KKM) scores. This makes it difficult for teachers to determine whether the material that has been explained can be understood because grades are not a benchmark for a student's success. In fact, if the teacher does not know a student's understanding, the teacher will have difficulty continuing to the next material. Implementation of the Multi-Criteria Recommender System (MCRS) can make it easier for teachers to predict whether students can progress to the next material and recommend which modules are suitable for these students. The recommendation system that will be built is in the form of web-based learning media so that students can be more interested and can help teachers improve learning outcomes. The method used is collaborative filtering by comparing adjusted cosine similarity, cosine based similarity and spearman rank order correlation. Based on the implementation of MCRS using the collaborative filtering method, it shows that the results of the recommendation system have a good impact on the teaching and learning process. Based on the 3 algorithms implemented, the best prediction result is cosine based similarity because the MAE value obtained is the lowest, namely 1.19 and the accuracy value is 76%.

Item Type:	Journal Article
Keywords:	Recommendation System; MCRS; Collaborative Filtering; Confusion Matrix; MAE
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	30 Apr 2024 14:23

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