Probability Prediction for Graduate Admission Using CNN-LSTM Hybrid Algorithm
DOI:
https://doi.org/10.33022/ijcs.v12i3.3248Keywords:
Probability Prediction, Graduate Admissions, Hybrid Learning, CNN-LSTM, DjangoAbstract
Currently, the prediction of student admissions still uses conventional machine learning algorithms where there is no algorithm for optimization. This study aims to produce a model that can predict student acceptance of ownership more optimally by using an optimization hybrid learning algorithm, namely the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). This study uses the Microsoft Team Data Science Process method which consists of business understanding, data acquisition & understanding, modeling, and implementation as well as using the acceptance dataset obtained from the kaggle.com website as much as 500 data. The results showed that the CNN-LSTM hybrid learning model could optimize the prediction of students' chances of success in exposure as evidenced by the evaluation results of RMSE of 6.31%, MAE of 4.4%, and R2 of 80.52%. This model is implemented in a website application using the Python language, the Django framework, and the MySQL database.
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Copyright (c) 2023 Burhanudin Zuhri, Nisa Hanum Harani, Cahyo Prianto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.