Published: 2023-07-01
Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap
DOI: 10.35870/jtik.v7i3.840
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Abstract
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).
Keywords
Stochastic Gradient Descent (SGD) ; Modification ; Performance
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 7 No. 3 (2023)
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Section: Computer & Communication Science
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Published: %750 %e, %2023
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License: CC BY 4.0
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Copyright: © 2023 Authors
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DOI: 10.35870/jtik.v7i3.840
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Adimas Tristan Nagara Hartono
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia
Hindriyanto Dwi Purnomo
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia
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