Application of Artificial Intelligence in Analysis of the Quality of Islamic Boarding School Education Using National Assessment Dataset and Education Report Card
Abstract
This study aims to explore the application of artificial intelligence (AI) in analyzing the quality of education in Islamic boarding schools, using the National Assessment Dataset and Education Report Card launched by the Ministry of Education and Culture. Although this dataset focuses on the formal education system in Indonesia, this study adapts the data to analyze the potential application of AI in improving the quality of pesantren education. A quantitative approach was used with the application of Python-based machine learning algorithms, including Logistic Regression and Random Forest, to analyze the impact of educational factors on the quality of learning in Islamic boarding schools. The results of the analysis showed that the Random Forest model had an accuracy of 87%, with a recall of 0.86, which suggests that this model is effective in detecting factors that contribute to the quality of education. In addition, the resulting ROC curve shows the performance of the model with an AUC value of 0.89, indicating the model's excellent ability to distinguish positive and negative classes in the data. The Confusion Matrix shows that the model has a low error rate in predicting the categories of student learning outcomes. From these results, it can be concluded that the application of AI, especially the Random Forest model, can be used to analyze the quality of education in Islamic boarding schools with high accuracy. This research proves that AI can be applied in Islamic boarding schools to improve understanding of the factors that affect education as well as provide data-driven recommendations for improvement. In addition, this research also shows that Waqf can play a role as a sustainable source of funding for the development of IT infrastructure that supports the implementation of AI in Islamic boarding schools.
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