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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Rizky Indrabayu
Universitas Muhammadiyah Malang
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.
Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques Setio Basuki; Rizky Indrabayu; Nico Ardia Effendy
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3328

Abstract

This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and Funding, Stigma, Service, Youth, and an additional label named Others. Second, it focuses on constructing a dataset of Indonesian tweets, totaling 29,068 data, by filtering tweets using the keywords "mental health" and "kesehatan mental". Third, this paper conducts data preprocessing and manual labeling of a random selection of 3,828 tweets, chosen due to the impracticality of labeling all data. Finally, the fourth stage involves conducting classification experiments using classical text features, non-contextual and contextual word embeddings, and performing topic detection experiments with three different algorithms. The experiments show that the BERT-based method achieved the highest accuracy, with 81% in the 'Others' vs. 'non-Others' classification, 80% in the seven main label classifications, and 92% in the seven main labels classification when using GPT-4-powered data augmentation. Topic detection experiments indicate that the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithms are more effective than the Hierarchical Dirichlet Process (HDP) in generating relevant keywords representing the characteristics of each main label.
Co-Authors Basuki, Setio Masatoshi Tsuchiya Nico Ardia Effendy Zamah Sari