The prediction of undergraduate student performance in chemistry course using multilayer perceptron - Che Akmal, Che Yahaya; Che Yahaya, Yaakub; Ahmad Firdaus, Zainal Abidin; Mohd Faizal, Ab Razak; Nuresa Fatin, Hasbullah; Mohamad Fadli, Zolkipli (IOP Publishing, 2020)
Chemical industries are the essential factors to convert from raw materials into the target products that we use in our daily life. This has brought a tremendous change in the way things operate. In the interest of demanding the chemical engineers is increasing, simultaneously the failure rate of a student in a chemistry course also rising. The students that register the chemistry course often fail whether in the first semester or following semester. Moreover, students also unable to recognize whether they can adapt in this course and graduate successfully. The objective of this research is to predict the students in the chemical course either quit or graduate in the future by using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. The accuracy of the results from this study is 92.23% percent.