Exploring Research Trends and Impact: A Bibliometric Analysis of RESTI Journal from 2018 to 2022

Abstract

This study provides a comprehensive analysis of the RESTI Journal, a prominent publication in the field of systems engineering and information technology. The analysis aims to evaluate the journal's publication output, citation impact, and overall contribution to the field. The study utilizes data from the Dimensions database, focusing on articles published between 2018 and 2022, resulting in a dataset of 594 articles. To analyze the collected data, the study employs bibliometric and network visualization tools such as Bibliometrix and VOSviewer. The analysis reveals a notable increase in the number of publications over time, indicating a growing interest and research activity in the field. Furthermore, the distribution of author productivity deviates from Lotka's law, highlighting variations in author patterns and productivity levels. An examination of institutional affiliations reveals Telkom University as the dominant institution, making a substantial contribution to the journal. Visualizations based on author-provided titles, abstracts, and keywords highlight research trends in image recognition and classification, with a particular emphasis on utilizing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). Overall, this study provides valuable insights into the performance and trends of the RESTI Journal. The findings contribute to a deeper understanding of the journal's impact and its role in advancing knowledge in systems engineering and information technology. These insights can inform researchers, practitioners, and stakeholders in the field, guiding future research directions and enhancing the scholarly impact of the RESTI Journal.