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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Psychology, Evaluation, and Technology in Educational Research
Anwas, Ihsan Maulana
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Computer Science & IT Education Social Sciences

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Quranic mushaf use and religious character: A meta-analysis Anwas, Ence Oos M.; Arlinwibowo, Janu; Nur, Ismail; Susanto, Juli; Shah, Imam; Miswanto, Miswanto; Aziz , Asep Rifqi Abdul; Anwas, Ihsan Maulana
Psychology, Evaluation, and Technology in Educational Research Vol. 8 No. 1 (2025)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/petier.v8i1.292

Abstract

This meta-analysis examines the association between use of the Qur’anic mushaf and the development of religious character and Qur’anic literacy. Studies were identified through Google searches and screened against a priori inclusion criteria. Following data extraction, pooled effects were estimated with a random-effects model. The analytic workflow comprised tests of heterogeneity, calculation of the summary effect size, assessment of publication bias, and moderator analyses. Across 33 studies (N = 2,209), the pooled correlation was r = 0.53, indicating a moderate-to-strong association between mushaf reading intensity (including memorization and habitual practice) and positive religious character outcomes—namely religiosity, student character traits, emotional intelligence, and learning behavior. These findings suggest that character-education initiatives should integrate structured Qur’anic literacy within both formal and non-formal settings, supported by equitable access to printed and digital mushaf and sustained guidance from teachers, religious instructors, and parents.
Performance Comparison of LSTM, XGBoost, and Residual-Correction Hybrid LSTM–XGBoost Models for Bitcoin Price Forecasting Anwas, Ihsan Maulana; Fahrianto, Feri; Shofi, Imam Marzuki; Ajif Yunizar Pratama
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5983

Abstract

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.
Co-Authors Ajif Yunizar Pratama Anwas, Ence Oos M. Arlinwibowo, Janu Aziz , Asep Rifqi Abdul Miswanto, Miswanto Nur, Ismail Shah, Imam Shofi, Imam Marzuki Susanto, Juli