Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap
DOI:
https://doi.org/10.35870/jtik.v7i3.840Keywords:
Stochastic Gradient Descent (SGD), Modification, PerformanceAbstract
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).
Downloads
References
Dahria, M., 2008. Kecerdasan Buatan (Artificial Intelligence). Jurnal Saintikom, 5(2), pp.185-197.
Giri, S., 2020. Writing Custom Optimizer in TensorFlow Keras API, https://cloudxlab.com/blog/writing-custom-optimizer-in-tensorflow-and-keras/ [Accessed at 28 Oktober 2021].
Wright, L. and Demeure, N., 2021. Ranger21: a synergistic deep learning optimizer. arXiv preprint arXiv:2106.13731.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Adimas Tristan Nagara Hartono, Hindriyanto Dwi Purnomo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
