Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap

Adimas Tristan Nagara Hartono (Universitas Kristen Satya Wacana)
Hindriyanto Dwi Purnomo (Universitas Kristen Satya Wacana)

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Vol 7 No 3 (2023): JULY-SEPTEMBER 2023

Stochastic Gradient Descent (SGD) is one of the commonly used optimizers in deep learning. Therefore, in this work, we modify stochastic gradient descent (SGD) by adding a fixed variable. We will then look at the differences between standard stochastic gradient descent (SGD) and stochastic gradient descent (SGD) with additional variables. The phases performed in this study were: (1) optimization analysis, (2) fix design, (3) fix implementation, (4) fix test, (5) reporting. The results of this study aim to show the additional impact of fixed variables on the performance of stochastic gradient descent (SGD).