Article Per Year (5 Year)
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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
Unknown Affiliation
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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Analisis Konseptual tentang Penerapan Teori Probabilitas Lanjut dalam Pengembangan Model Statistik Modern Hanggoro, Dimas Banu Dwi
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1450

Abstract

This study aims to examine the application of advanced probability theory in the development of modern statistical models through a Systematic Literature Review approach to 25 scientific articles published in 2020–2025. This study is complemented by a bibliometric analysis using VOSviewer to explore the relationship of terms, temporal trends, and concept density in the literature. The visualization results show that the terms "model" and "probability" are the main nodes in concept development, with the strengthening of the themes of distribution, uncertainty, and cross-disciplinary applications. The application of probability theory is seen dominantly in the fields of engineering, environment, transportation systems, and human behavior. The study also shows a shift in focus from classical risk distribution to more complex and contextual predictive modeling. The emergence of new terms such as "stack effect", "reaction time", and "p pod method" marks the growing interest in the application of probability in physical simulations and advanced technical systems. These results strengthen the position of probability theory as an adaptive conceptual framework in responding to the challenges of modern data analysis and uncertainty.
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.
Analisis Komparatif Arsitektur Deep Learning Untuk Aplikasi Computer Vision: Studi Literature Review Hanggoro, Dimas Banu Dwi
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.542

Abstract

The development of computer vision technology has undergone a significant transformation with the emergence of increasingly sophisticated deep learning architectures. This study aims to conduct a comparative analysis of the characteristics, performance, and computational efficiency of seven prominent Convolutional Neural Network (CNN) architectures: LeNet-5, AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, and MobileNet, within the scope of modern computer vision applications. A systematic literature review was employed as the research methodology, analyzing scientific publications published between 2021 and 2025 from reputable databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. The findings reveal that each architecture possesses unique strengths and trade-offs. LeNet-5 is effective for simple tasks; AlexNet introduced innovations such as the ReLU activation function and dropout regularization; VGG is notable for its network depth; GoogLeNet achieves efficiency through its Inception modules; ResNet addresses the vanishing gradient problem using skip connections; while SqueezeNet and MobileNet are optimized for mobile applications with limited computational resources. The study concludes that no single architecture is universally superior. Instead, optimal model selection depends on balancing accuracy, computational efficiency, and the specific resource constraints of the intended application.
Quality Analysis of Low-Code No-Code Application Development Using ISO/IEC 25010 Standard: A Systematic Literature Review Hanggoro, Dimas Banu Dwi; Kurniawati, Laela; Rianto, Yan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1633

Abstract

The acceleration of digital transformation has increased the demand for fast and efficient software development. This condition has led to the emergence of Low-Code/No-Code (LC/NC) approaches, which allow users with limited technical expertise to build applications through visual interfaces and pre-built components. This study aims to analyze the quality of Low-Code No-Code (LCNC) application development based on the ISO/IEC 25010 standard through a Systematic Literature Review (SLR) method, supported by bibliometric visualization using VOSviewer software. The reviewed articles were published between 2020 and 2024. The visualization results indicate that keywords such as context, development, and system are the primary focus in the study of LCNC application quality. Furthermore, the findings of Meira et al. (2020) demonstrate that the Analytical Hierarchy Process (AHP) method, when combined with the ISO/IEC 25010 standard, is effective in evaluating and comparing software system quality within an industrial context. This study concludes that a standards-based evaluation approach, complemented by bibliometric visualization, can provide valuable insights for decision-making processes related to the development and implementation of LCNC applications.
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