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A proposed framework in an intelligent recommender system for the college student
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This article aims to proposed framework an Intelligent Recommender System (IRS)
for students in higher education institutions. This conceptual framework includes problems in
predicting student performance, the possibility of graduating on time, and recommends choosing
subjects according to performance, and career interests, which are useful for assisting
pedagogical interventions in future student development. The success in the development and
implementation of the proposed IRS framework is inseparable from using data mining and
machine learning techniques in predicting and providing recommendations. Data analysis
consisted of clustering techniques, association rules, and classification using Support Vector
Machine (SVM), Naïve Bayes, and k-Nearest Neighbour (k-NN). These techniques are used to
solve problems related to students and to provide appropriate recommendations. The result is an
IRS conceptual framework for the college student that can be used as smart agents to provide
student guidance and suggestions to support the process of education in higher education
D Kurniadi - Personal Name
Edi Abdurachman - Personal Name
H L H S Warnars - Personal Name
W Suparta - Personal Name
Edi Abdurachman - Personal Name
H L H S Warnars - Personal Name
W Suparta - Personal Name
NONE
Journal of Physics: Conference Series 1402 (2019) 066100
electronic file
English
IOP Publishing
2019
Jil.1402, Terbitan 6, Hlm.066100
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