Jurnal
Prediction Model Grade Point Average using Backpropagation Neural Network and Multiple Linear Regression
- Bagikan:
Education in the 21st century equips students with knowledge and information
and the success of achieving academic achievements during the learning process.
Students' academic achievement can be seen from various aspects: the Grade Point
Average. So far, efforts to predict GPA have not been made. In fact, if the student's
Grade Point Average can be predicted from an early age, the study program can
implement a policy to improve graduates' quality and make planning, study escort,
and guidance more intensive. Based on this urgency, this study aims to produce a
predictive model for the GPA of STMIK Amik Riau students in the odd semester
of 2019, using the Backpropagation Neural Network algorithm and Multiple
Linear Regression. Backpropagation's architectural model is 8 architectures, and
4-5-1 is the best architectural model with MSE at the time of training =
0.00099965532 and MSE during network validation = 0.0038793 with an epoch
of 102 iterations and the resulting accuracy value of 95.24%. Meanwhile, the GPA
prediction results, after testing using the Multiple Linear Regression algorithm,
obtained an MSE value of 0. 0.27966667%, with a Multiple Correlation
coefficient (R) of R = 0.9774925 and a coefficient of determination (R2) =
0.95549159. Thus the prediction of student GPA using MLR is accurate because
the value of the coefficient of determination (R2) is close to 1.
Detail Information
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Jurnal
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| Kode Prodi PDDIKTI | |
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Published
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| Bahasa |
English
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| Penerbit | Telematika : Purwokerto., 2021 |
| Edisi |
Published
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| Subyek | |
| No Panggil | |
| Copyright |
Universitas Amikom Purwokerto
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| Doi |