Machine Learning Methods for Academic Achievement Prediction: A Bibliometric Review - Nugraha, Fajar; Widowati, Widowati; Sugiharto, Aris (Jurnal Sistem Informasi Bisnis, 2025)
This study examines research trends regarding the prediction of academic achievement using machine learning. Research in the field of academic achievement is currently continuing to develop, but has not been explored comprehensively in a bibliometric context. The visualization provided includes a map of publication development using machine learning methods based on country, analysis of bibliographic pairs and keywords used. To find out the visualization results, bibliographic analysis was used using VOSviewer. The data used in this analysis were 76 articles collected from the Scopus database from 2018-2023. From the results of the analysis, it is known that research related to academic achievement still shows a growing trend in publications in the field of discussion of factors or predictors that influence academic achievement as well as research that proposes or evaluates models for predicting academic achievement. The research results show that although machine learning techniques such as Random Forest and Support Vector Machine are often used in academic achievement prediction research. Future research could consider developing a more adaptive and comprehensive approach regarding the contribution of specific factors that influence the accuracy of more in-depth prediction models in this field.