Manajemen Pembelajaran Tahfidzul Quran Berbasis Metode Yaddain Di Mi Plus Darul Hufadz Sumedang
Bibliographic record
Abstract
Management of tahfidz al-Quran learning in terms of organizing educators has a lack of human resources. The implementation of tahfidz learning in practice has not been effectively evenly implemented by educators. This study aims to uncover the processes of planning, organizing, implementing, evaluating, of the tahfidz al-Quran learning based on the Yaddain method in the Madrasah Ibtidaiyah Plus Darul Hufadz Sumedang. The research method used is qualitative. Data collection techniques used the technique of in-depth interviews, observation, and documentation study. The results of the study show: first, planning is carried out by making learning concepts that are detailed with short-term, mid-term, and long-term planning, formulated through syllabi and Learning Implementation Plans (RPP); second, organizing is carried out by determining the tasks and stages in the tahfidz Quran learning process; third, the implementation is carried out with class management, scheduling, activity mechanisms including opening, core and closing activities; fourth, evaluation is carried out by monitoring students with individual student absenteeism while taking part in learning, repeating mid-semester and final examinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".