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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Muchtar Ali Anwar
Universitas Pamulang, Tangerang Selatan
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Implementasi Algoritma Random Forest untuk Prediksi Waktu Penyelesaian Hafalan Al-Qur’an Berbasis Website Muchtar Ali Anwar; Sholihin Sholihin; Muhammad Nur Fajriansyah; Wisnu Chairin
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9832

Abstract

Manual monitoring of Quranic memorization (tahfizh) in Islamic boarding schools faces efficiency challenges due to large student populations and paper-based record keeping. This study aims to implement the Random Forest algorithm to predict the estimated completion time of Quranic memorization in a web-based monitoring system at Madrasah Aliyah Jam’iyyah Islamiyyah, Tangerang Selatan, Indonesia. The dataset consists of 12,458 memorization logs from 271 students during March 1 to May 3, 2026. Feature engineering produced 15 features covering Quranic text complexity, student memorization history, and temporal patterns; Spearman correlation feature selection reduced these to 13 significant features. The model was optimized using GridSearchCV and evaluated with MAE, RMSE, R², MAPE, and 5-fold cross-validation. Random Forest achieves R²=0.8966, MAE=0.6141, and MAPE=6.98% on the 70:30 split, outperforming Decision Tree (R²=0.8879) and matching XGBoost (R²=0.8964). Cross-validation yields CV R²=0.9004, confirming stable generalization. Feature importance analysis indicates that student learning habits are stronger predictors than Quranic text complexity. As a practical contribution, the model is integrated into a web-based monitoring system enabling teachers to track all students’ progress centrally and receive automated memorization completion estimates, enhancing the effectiveness of guidance in tahfizh institutions.
Co-Authors Muhammad Nur Fajriansyah Sholihin Sholihin Wisnu Chairin