Research Trend on Data Mining Using Bibliometric Analysis with VOSviewer - Dika Putra Wijaya; Mohammad Kawtsar; Manon Guinny; Ganesh Ganesh; Nur Laila; Wirda Amirotul Amiroh (LogicLink, 2026)
This study aims to analyze global research trends in data mining using bibliometric analysis. The rapid development of information technology has transformed data mining into a crucial tool for various industrial sectors to extract knowledge from large databases. The research method used is a qualitative descriptive approach with a bibliometric approach, assisted by Publish or Perish (PoP) software for data collection from ScienceDirect and Google Scholar databases. Data visualization and mapping were performed using VOSviewer to identify topic clusters, temporal developments, and research density between January 2022 and December 2026. The analysis results indicate the existence of five main clusters: technical aspects of algorithms (red), technological and industrial infrastructure (green), geographic applications and environmental impacts (blue), causality analysis (yellow), and literature synthesis (purple). Overlay visualization reveals a shift in trends from mastery of basic algorithm infrastructure (such as random forests and big data) to a critical evaluation phase focused on risk mitigation, research gap identification, and practical application in the real world. This study provides a strategic overview for researchers to identify collaboration opportunities.