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- W3161379002 abstract "Dropout is considered a big problem affecting universities in Indonesia. Deciding to dropout is not easy, because universities have to look at various academic parameters or criteria. The solution to simplify these problems is by utilizing knowledge of data mining techniques or machine learning in education. The classification approach technique using Neural Network (NN) method to predict the academic status of a student early can provide optimal results. Before forming the NN model, the supporting data will go through data pre-processing using the mean/average method, z-score normalization and information gain to obtain the best parameters. Furthermore, Adam optimizer is also used to optimize a parameter, the optimization will update the weights iteratively based on the training data. The results obtained from this prediction model are calculated using cross validation as the benchmark of the method used. The results obtained reaches a precision of 0.937. The biggest factor that has an influence on dropout possibility is grade, followed by failed courses, then the student’s absence, even the age of the student that also has a big effect." @default.
- W3161379002 created "2021-05-24" @default.
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- W3161379002 date "2021-04-09" @default.
- W3161379002 modified "2023-09-23" @default.
- W3161379002 title "Predicting Student’s Failure in Education Based on Dropout Status" @default.
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- W3161379002 doi "https://doi.org/10.1109/eiconcit50028.2021.9431905" @default.
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