Academic achievement prediction model using artificial neural network: a case study of UiTM Melaka Diploma in Computer Science Students / Normaziah Abdul Rahman, Fadhlina Izzah Saman and Nurulhuda Zainuddin

Neural network has emerged as a very popular area of research, both from the design and the usage points of view. It can be used to do pattern recognition and classification, prediction and control and conceptual information management. With these strengths, it could be applied to develop a model for predicting the Computer Science student's academic performance at Universiti Teknologi Mara Kampus Melaka based on their admission requirement subjects. The model will analyze a trend of past students' achievement at point of graduation in whether they graduated with a CGPA above or less than 3.00, and as a result, it is able to predict the future students' achievement. Based on the experiment, the number of students predicted to graduate with CGPA of 3.00 or above is 12 and the number of students predicted to graduate with a CGPA below 3.00 is 26.These results are very important to the faculty so that students can be steered in the right way to achieve a CGPA of at least 3.00 hence achieving the Quality Objective of UiTM Melaka which is to achieve at least 65% of fulltime students graduating with a CGPA of at least 3.00 in 2011.