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Analysis of Deep Learning Method Development for Performance Optimization of Complex Data Classification Models

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Abstract

This study aims to analyze the development of deep learning methods for optimizing complex data classification model performance through a Systematic Literature Review (SLR) approach examining 25 Scopus-indexed scientific articles published between 2024 and 2025. The analysis employs bibliometric techniques using VOSviewer to map keyword networks, temporal trends, and term density patterns. Visualization results identify three primary clusters: (1) LSTM-based classification and intrusion detection systems in cybersecurity applications; (2) CNN optimization and model efficiency for medical imaging and satellite image classification; and (3) artificial intelligence integration with visual classification and evolutionary optimization algorithms. Recent trends demonstrate the dominance of keywords such as "optimization," "effectiveness," and "feature selection," alongside growing interest in hybrid approaches and metaheuristic algorithms. This research provides a comprehensive overview of methodological transformations and application directions of deep learning in complex data classification domains. These findings are expected to serve as strategic references for advancing research and applications in big data-driven artificial intelligence technologies.

Keywords

Deep Learning; Data Classification; Optimization; CNN; LSTM

References

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Author Biographies

Dimas Banu Dwi Hanggoro

Universitas Nusa Mandiri

Universitas Nusa Mandiri, Central Jakarta City, Special Capital Region of Jakarta, Indonesia

How to Cite

Hanggoro, D. B. D. (2025). Analysis of Deep Learning Method Development for Performance Optimization of Complex Data Classification Models. Journal Innovations Computer Science, 4(1), 49-59. https://doi.org/10.56347/jics.v4i1.242
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Article Details

  • Volume: 4
  • Issue: 1
  • Pages: 49-59
  • Published: 2025-05-30
  • Section: Article

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