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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal Innovations Computer Science
Hanggoro, Dimas Banu Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Journal Innovations Computer Science

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Analysis of Deep Learning Method Development for Performance Optimization of Complex Data Classification Models Hanggoro, Dimas Banu Dwi
Journal Innovations Computer Science Vol. 4 No. 1 (2025): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i1.242

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.
Analysis of Deep Learning Method Development for Performance Optimization of Complex Data Classification Models Hanggoro, Dimas Banu Dwi
Journal Innovations Computer Science Vol. 4 No. 1 (2025): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i1.242

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.
Co-Authors Laela Kurniawati, Laela Rianto, Yan