Open AccessRETRACTED: The prediction of undergraduate student performance in chemistry course using multilayer perceptron
Author(s) -
Che Akmal Che Yahaya,
Che Yahaya Yaakub,
Ahmad Firdaus Zainal Abidin,
Mohd Faizal Ab Razak,
Nuresa Fatin Hasbullah,
Mohamad Fadli Zolkipli
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/769/1/012027
Subject(s) - disappointment , computer science , pace , perceptron , course (navigation) , machine learning , artificial intelligence , data science , artificial neural network , engineering , psychology , social psychology , geodesy , aerospace engineering , geography
Chemical industry are key elements for changing crude materials to our ordinary objective merchandise. This has achieved an immense move in how things work. The disappointment pace of an understudy in a science course is additionally expanding with regards to requesting the compound specialists. Understudies who enlist the science course regularly bomb in the first or consequent semesters. Moreover, understudies are likewise unfit to comprehend in the event that they can adjust and graduate effectively in this program. The objective of this exploration is to foresee the future utilization of improved science for understudies to fizzled or graduate by upgraded Multilayer Perceptron (MLP) arrangement with Adaboost. The exactness of the outcomes is 92.23% percent.