Prediksi Prestasi Mahasiswa Dengan Menggunakan Algoritma Backpropagation

Abstract

This study aims to overcome the problems in predicting student achievement at the Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done by applying Backpropagation algorithm and implement it into Matlab software. Backpropagation algorithm is one of the methods on artificial neural networks that is quite reliable in solving problems including prediction. In this study conducted on the object of students semester One with a lot of data samples 26 samples. The data sample is divided into two parts, 70% of the data is used as training data and 30% of the data is used as testing data. This study uses ten architectural models, namely 9-2-1, 9-3-1, 9-4-1, 9-5-1, 9-6-1, 9-7-1, 9-8-1, 9-9-1, 9-10-1, 9-11-1. Of the ten Backpropagation network architecture models implemented in predicting student achievement in Matlab software obtained the best output is 9-2-1 pattern with epoch 8149, time duration for 17 seconds, and MSE (error rate) value of 2.80 e-05 for training and MSE (error rate) of 0.1248 with accuracy of 87.5% for testing. The best architecture obtained is expected to be used as a picture by the academic Polytechnic Business Indonesia (PBI) in predicting student achievement.

Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian

“…Kelebihan dari metode backpropagation yaitu yang bersifat adaptive dan fault tolerance [14]. Penelitian sebelumnya telah dilakukan dengan menggunakan metode backpropagation yaitu prediksi prestasi mahasiswa politeknik bisnis indonesia (PBI) dengan membangun 10 model arsitektur pembagian data 70:30 didapatkan arsitektur terbaik pada pola 9-2-1 dengan epoch 8149 dan nilai MSE 0.1248 dengan akurasi 87,5% untuk pengujian [15]. Prediksi jumlah pasien sembuh COVID-19 dengan melatih model arsiktektur 4-4-1 jumlah data 25.993 menghasilkan akurasi sebesar 90% [16].…”