Utilizing Social Media as a Means of Memorizing the Qur'an
Bibliographic record
Abstract
Social media is a change from increasingly sophisticated technology. the influence of social media is very much in the community, the positive influence is one of which makes it easier to memorize the Al-Qur'an. Memorizing the Al-Quran is an action taken by someone to increase their memorization of reading without looking at the Al-Qur'an or text. Researchers conducted research at the Esa Unggul Integrated Islamic Elementary School in Pekanbaru. The purpose of the researchers in conducting this research was to find out how teachers apply social media as a means of memorizing the Qur'an for students at the Esa Unggul Integrated Islamic Elementary School, Pekanbaru. This school uses the wahdah method in memorizing the Qur'an. in this study the researcher used a descriptive method with a qualitative approach. techniques used by researchers in collecting data is by way of interviews, observation and documentation. The subjects in this study were school principals, teaching staff, and students. The results of research at the Esa Unggul Integrated Islamic Elementary School in Pekanbaru can be said to be effective, by using social media the students are very enthusiastic in memorizing the Qur'an. this is a new way to apply in schools. Here the teacher has its own challenges in using social media to serve as a means of memorizing the Qur'an. Because in general children use social media to play.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".