Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap
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Jul 1, 2023
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Stochastic Gradient Descent (SGD), Modification, Performance
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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).
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Hartono, A. T. N., & Purnomo, H. D. (2023). Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(3), 359–367. https://doi.org/10.35870/jtik.v7i3.840
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Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Bengio, Y. and LeCun, Y., 2007. Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), pp.1-41. DOI: https://doi.org/10.1038/nature14539.
Nwankpa, C.E., 2020. Advances in optimisation algorithms and techniques for deep learning. Advances in Science, Technology and Engineering Systems Journal, 5(5), pp.563-577. DOI: https://doi.org/10.25046/aj050570.
Sazli, M.H., 2006. A brief review of feed-forward neural networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 50(01). DOI: https://doi.org/10.1501/commua1-2_0000000026.
Rinanto, N., Wahyudi, M.T. and Khumaidi, A., 2018. Radial basis function neural network sebagai pengklasifikasi citra cacat pengelasan. Rekayasa, 11(2), pp.118-131. DOI: https://doi.org/10.21107/rekayasa.v11i2.4418.
Wibawa, A.P., Lestari, W., Utama, A.B.P., Saputra, I.T. and Izdihar, Z.N., 2020. Multilayer Perceptron untuk Prediksi Sessions pada Sebuah Website Journal Elektronik. Indonesian Journal of Data and Science, 1(3), pp.57-67. DOI: https://doi.org/10.33096/ijodas.v1i3.15.
Ibrahim, H.S., Jondri, J. and Wisesty, U.N., 2018. Analisis Deep Learning Untuk Mengenali Qrs Kompleks Pada Sinyal Ecg Dengan Metode Cnn. eProceedings of Engineering, 5(2).
Faishol, M.A., Endroyono, E. and Irfansyah, A.N., 2020. Predict Urban Air Pollution in Surabaya Using Recurrent Neural Network–Long Short Term Memory. JUTI J. Ilm. Teknol. Inf, 18(2), p.102-114. DOI: http://dx.doi.org/10.12962/j24068535.v18i2.a988.
Kala, R., Vazirani, H., Shukla, A. and Tiwari, R., 2010. Medical Diagnosis using Incremental Evolution of Neural Network. Journal of Hybrid Computing Research, 3(1), pp.9-17.
Wikarta, A., Pramono, A.S. and Ariatedja, J.B., 2020, December. Analisa Bermacam Optimizer Pada Convolutional Neural Network Untuk Deteksi Pemakaian Masker Pengemudi Kendaraan. In Seminar Nasional Informatika (SEMNASIF) (Vol. 1, No. 1, pp. 69-72).
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