Student Graduation Prediction System For Faculty Of Saintek Muhammadiyah Sidoarjo University Using Backpropagation Artificial Neural Network Method
Sistem Kelulusan Mahasiswa Fakultas Saintek Universitas Muhammadiyah Sidoarjo Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation
DOI:
https://doi.org/10.21070/ups.7517Keywords:
predicting, backpropagation, Artificial Neural NetworkAbstract
universities in Sidoarjo, such as the Muhammadiyah University of Sidoarjo (UMSIDA), the graduation rate of students can have an impact on the accreditation of study programs. considering the importance of accreditation in student graduation. Predicting student graduation allows for adequate preparation and support for students to successfully complete their studies. This research utilizes an Artificial Neural Network (JST) with a backpropagation approach to predict student graduation. The input data for this JST training comes from the Faculty of Science and Technology, Muhammadiyah University of Sidoarjo (UMSIDA) regarding student graduation rates from 2015 to 2019. The test results show that the Mean Square Error (MSE) in the JST output is 0.000141295, in accuracy testing The accuracy value was 93.428901%. This shows that the backpropagation method with ANN can be used effectively to predict student graduation.
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