(Universiti Teknikal Malaysia Melaka, Malaysia)(2) Jesmeen Mohd Zebaral Hoque
(Multimedia University, Malaysia, Malaysia)(3) Md. Jakir Hossen
(Multimedia University, Malaysia, Malaysia)(4) Halizah Basiron
(Universiti Teknikal Malaysia Melaka, Malaysia)(5) Chy. Mohammed Tawsif Khan
(Multimedia University, Malaysia, Malaysia)*corresponding author
AbstractThe university admission test is an arena for students in Bangladesh. Millions of students have passed the higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue their further study. It is challenging for a student to prepare all these three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important rather than following the trend. This study developed a preliminary system to predict a suitable admission test category by evaluating students' early academic performance through data collecting, data preprocessing, data modelling, model selection, and finally, integrating the trained model into the real system. Eventually, the Neural Network was selected with the maximum 97.13% prediction accuracy through a systematic process of comparing with three other machine learning models using the RapidMiner data modeling tool. Finally, the trained Neural Network model has been implemented by the Python programming language for opinionating the possible option to focus as a major for admission test candidates in Bangladesh. Keywordsartificial intelligence; machine learning; artificial neural network; students' major prediction bangladesh; major selection; model selection
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DOIhttps://doi.org/10.26555/ijain.v11i2.1490 |
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