OPTIMASI MENGGUNAKAN ALGORITMA STOCHASTIC GRADIENT DESCENT (SGD): STUDI BIBLIOMETRIK

Authors

  • Ginda Maruli Andi Siregar Universitas Samudra
  • Muhammad Farid Fitriansyah Universitas Samudra

DOI:

https://doi.org/10.30587/indexia.v7i1.9708

Keywords:

Stochastic Gradient Descent (SGD), optimasi, pembelajaran mesin, bibliometrik, tren penelitian, klaster kata kunci

Abstract

Penelitian ini bertujuan untuk menganalisis perkembangan riset mengenai algoritma Stochastic Gradient Descent (SGD) dalam konteks optimasi pembelajaran mesin menggunakan pendekatan bibliometrik. Sebanyak 55 artikel ilmiah yang dipublikasikan antara tahun 2020 hingga 2025 dianalisis untuk mengidentifikasi tren publikasi, kolaborasi penulis, kata kunci dominan, dan struktur tematik. Hasil kajian menunjukkan peningkatan signifikan publikasi terkait SGD, dengan fokus utama pada topik deep learning, optimization, dan generalization. Sebagai upaya untuk memperdalam pemahaman terhadap dinamika tersebut, penelitian ini menyertakan analisis visual menggunakan perangkat lunak bibliometrik VOSviewer dan Biblioshiny. Hasil visualisasi mengungkapkan lima klaster utama yang mencerminkan arah dan kedalaman riset SGD, serta tantangan yang dihadapi seperti sensitivitas terhadap learning rate dan kestabilan model. Temuan ini juga membuka peluang untuk penerapan algoritma SGD pada teknologi edge AI, mobile learning, serta sistem pembelajaran di daerah dengan keterbatasan infrastruktur.

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Published

2025-05-19

How to Cite

Siregar, G. M. A., & Fitriansyah, M. F. (2025). OPTIMASI MENGGUNAKAN ALGORITMA STOCHASTIC GRADIENT DESCENT (SGD): STUDI BIBLIOMETRIK. Indexia, 7(1), 53–68. https://doi.org/10.30587/indexia.v7i1.9708

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