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All Journal Edu Research : Jurnal Penelitian Pendidikan Information Technology Education Journal
Oki Derajat Sudarmojo
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Humanities Computer Science & IT Education Other

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PEMANFAATAN BIG DATA UNTUK MENGANALISIS DAN MENINGKATKAN PEMBELAJARAN MATEMATIKA Fanny Adibah; Al Ikhlas; Oki Derajat Sudarmojo; Tsuwaibatul Aslamiyah; Muhamad Saleh; Joni Wilson Sitopu
EDU RESEARCH Vol 6 No 1 (2025): EDU RESEARCH
Publisher : IICLS (Indonesian Institute for Corporate Learning and Studies)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47827/jer.v6i1.532

Abstract

The rapid development of technology, particularly in the field of Big Data, has provided new opportunities to enhance learning processes, including mathematics education. This study explores the utilization of Big Data analytics to analyze and improve mathematics learning outcomes. By processing large-scale educational data, valuable insights can be obtained to identify students' learning patterns, predict academic performance, and develop personalized learning strategies. The research employs a data-driven approach, utilizing machine learning techniques and statistical analysis to examine student performance trends. The findings indicate that the integration of Big Data in mathematics education enables more precise interventions, adaptive learning models, and data-supported decision-making for educators. This research highlights the significance of data analytics in addressing learning challenges, optimizing teaching methodologies, and fostering better educational outcomes. Future studies are recommended to explore more advanced analytical techniques and expand the scope of implementation in various educational settings.
Short-Term IHSG Closing Price Prediction Using Random Forest Hernita, Ayu; Oki Derajat Sudarmojo; Sabarudin Saputra; Nur Alinuddin Kaharu; Wildan
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.9549

Abstract

Predicting stock market prices is challenging due to the complex and volatile nature of financial time series. This study examines the use of Random Forest Regression (RFR) to predict the closing prices of the Jakarta Composite Index (IHSG) from January 2015 to May 2025. Historical data were collected from Yahoo Finance, preprocessed, and engineered into seven predictor features, including lagged prices, moving averages, volatility measures, and a COVID-19 event indicator.The dataset was split into training and testing sets (80:20) using a time-based approach. Hyperparameters were optimized via RandomizedSearchCV with TimeSeriesSplit cross-validation. The final model achieved an RMSE of 177.55 and an R² of 0.71 on the testing set, demonstrating strong predictive performance. Feature importance analysis indicated that the previous day’s closing price (lag_1) was the most influential predictor, followed by lag_2 and MA_7.Visualizations showed that the model effectively captured major trends and turning points, with minor deviations during extreme volatility. The next-day prediction for May 23, 2025, yielded a closing price of 7145.12, indicating practical applicability for short-term investment decisions. The results highlight that Random Forest Regression is a robust and effective method for predicting financial time series, capable of handling non-linear patterns and market fluctuations
Co-Authors Adibah, Fanny Al Ikhlas Ayu Hernita Muhamad Saleh Nur Alinuddin Kaharu Sabarudin Saputra Tsuwaibatul Aslamiyah Wildan