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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Husna, Mardhiatul
Politeknik Negeri Medan, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Integration of ARIMA Models and Machine Learning for Academic Data Forecasting: A Case Study in Applied Mathematics Simanungkalit, Erwinsyah; Husna, Mardhiatul; Tarigan, Jenny Sari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24148

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.
Co-Authors Simanungkalit, Erwinsyah Tarigan, Jenny Sari