Prediction of Indonesian Learning Achievement Using Machine Learning Models
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Educational Technology, Learning Achievement, Machine Learning
Abstract
Background. Student learning achievement is one of the important indicators in assessing the effectiveness of education. Various factors such as student attendance and socioeconomic status have been known to affect learning outcomes. However, the influence of access to technology in the context of education in Indonesia has not been studied in depth. In today's digital era, access to technology is an important aspect that can support or hinder the learning process of students.
Purpose. This study aims to analyze the influence of student attendance, socioeconomic status, and access to technology on student learning achievement. In addition, this study also aims to test the accuracy of machine learning models in predicting student exam results based on these variables.
Method. This study uses a quantitative approach with the application of machine learning models, including linear regression and decision trees. The data used includes students' test scores, attendance levels, socioeconomic status, and access to technology devices and networks.
Results. The results of the analysis showed that student attendance, socioeconomic status, and access to technology had a significant influence on learning achievement. The machine learning model applied is able to predict students' exam results with a high level of accuracy, demonstrating the effectiveness of this approach in educational analysis.
Conclusion. This study emphasizes the importance of external factors, especially access to technology, in predicting student learning achievement. A more inclusive education policy is needed by expanding access to technology and educational facilities, in order to support the equitable distribution of learning quality in all circles.
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