Integration of ARIMA Models and Machine Learning for Academic Data Forecasting: A Case Study in Applied Mathematics

Erwinsyah Simanungkalit, Mardhiatul Husna, Jenny Sari Tarigan

Abstract


This study explores the use of ARIMA models and machine learning algorithms, specifically Random Forest and Multiple Linear Regression, to predict student academic performance. A mixed-method approach analyzed academic grades data from the past three years, with ARIMA identifying time series trends and machine learning models predicting academic outcomes based on various variables. Results show ARIMA effectively maps academic trends, while Random Forest excels in handling complex relationships, with an RMSE of 1.12 and an MAE of 0.94. These findings highlight the potential of combining statistical models and machine learning in developing adaptive learning strategies and data-driven decision-making. This approach offers a robust framework for improving educational outcomes and can guide future research in predictive analytics for educational systems.

Keywords


ARIMA; Machine Learning; Academic Data; Predictive Analytics;

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DOI: http://dx.doi.org/10.30829/zero.v9i1.24148

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