A Visualized Bibliometric Analysis for Mapping Research Trends of Machine Learning in Academic Research

Z. S. Ibrahim, Fina Rifqiyah, Jamal Mohamad Sultan, Heri Retnawati, Jumriani Sultan

2023

Full text Open access: Gold Metadata (CSL-JSON)

DOI 10.31643/2023.34

Data confidence 85 high

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This study aims to conduct a comprehensive bibliometric analysis of machine learning research within the academic research from 2013 to 2023. The goals include comprehending how publication trends have changed over time, pinpointing important fields of interest, and clarifying how machine learning and scholarly research interact. This study's methodology combines a bibliometric framework with a qualitative descriptive approach. The data were collected from Scopus database, which consist of 4.655 documents for analysis. The analysis is executed using R studio and Biblioshiny. The result shows that the rise of machine learning in academic research between 2013 and 2023 suggests that this field of study will continues to be a trending area for academic exploration.