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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika
Masatoshi Tsuchiya
Toyohashi University of Technology
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Detecting Research Evolution and Trends in The Computer Vision Domain using Topic Modeling and Large Language Models Setio Basuki; Zamah Sari; Rizky Indrabayu; Masatoshi Tsuchiya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2098

Abstract

Research evolution and trends in computer vision (CV) are important for understanding the field’s land-scape. Trends show which topics are gaining attention, while evolution reveals how those topics change over time. Understanding both helps researchers gain insight into CV and anticipate emerging areas of focus. How-ever, the rapid growth of publications makes such detection challenging. This paper aims to detect research evolution and trends in CV using topic modeling (TM) and large language model (LLM) techniques. The study applies TM and LLM approaches to papers from leading CV conferences, Computer Vision and Pattern Recog-nition (CVPR), International Conference on Computer Vision (ICCV), and Winter Conference on Applications of Computer Vision (WACV), published between 2013 and 2023, totaling more than 21,000 papers, using only abstracts and titles. The TM methods used are Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), which generate keywords that represent topics. LLMs then refine these topics to support better analysis. The results show that research evolution and trends are easier to identify from abstracts than from titles, with BERTopic outperforming LDA in internal va-lidity based on coherence metrics and external validity based on human judgment. In addition, the topics evolved from traditional image processing tasks in earlier years to a stronger focus on deep learning and, more recently, generative approaches. Integrating TM techniques with LLMs enhances the detection of evolving re-search themes and trends in CV. This approach provides a clearer understanding of the field's development and helps anticipate future directions.
SC-Literature Intelligence: A Retrieval-Augmented Generation Framework for Multi-Category AI Literature Synthesis in Supply Chain Setio Basuki; Amelia Khoidir; Muhammad Ilham Perdana; Muhammad Daffa Nugraha; Masatoshi Tsuchiya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16477

Abstract

This paper develops SC-Literature Intelligence, a retrieval-augmented generation (RAG) framework for research synthesis of scientific literature on artificial intelligence (AI) in the supply chain domain. The study addresses the fragmentation of scientific findings, which makes cross-document understanding difficult by supporting four categories of literature-analysis queries: trend analysis, gap detection, comparative synthesis, and evidence-based question answering (QA). The primary novelty lies in introducing a category-aware research synthesis framework capable of evaluating RAG performance across multiple literature-analysis tasks rather than conventional question answering. The framework is built from Scopus-indexed abstracts through pre-processing, chunk-based embedding using BGE-M3 and LaBSE, vector storage, semantic retrieval, and prompt-guided generation evaluated using the RAGAS framework across 640 experimental runs. The results show that BGE-M3 consistently outperforms LaBSE on all RAGAS indicators with the best configuration (chunk size 64, Top-K 5) achieving scores between 0.722 and 0.856 across faithfulness, answer relevancy, context precision, and context recall. Gap detection emerges as the best-supported query category, whereas comparative synthesis remains the most challenging. Failure analysis further reveals that retrieval-stage issues dominate over generation-stage issues, identifying embedding quality as the primary bottleneck. These findings demonstrate that category-aware RAG-based synthesis can support structured, evidence-grounded literature analysis in the supply chain AI domain.
Co-Authors Amelia Khoidir Basuki, Setio Muhammad Daffa Nugraha Muhammad Ilham Perdana Rizky Indrabayu Zamah Sari