Efforts To Improve The Recognition Of Hijaiyah Letters Through The Tilawati Method In Group A Children At Aba 55 Kindergarten In Semarang
Bibliographic record
Abstract
This study aims to improve the ability to recognize hijaiyah letters in Group A children at ABA 55 Semarang Kindergarten through the application of the Tilawati method. The background of this research is the low ability of children to recognize and pronounce hijaiyah letters, which is caused by learning methods that are less interesting and not in accordance with the characteristics of early childhood development. Learning that is monotonous and less varied causes children to get bored easily and not focus on recognizing hijaiyah letters. Therefore, a more interactive, fun, and appropriate approach is needed for the child's developmental stages. This study uses the Kemmis and McTaggart model Class Action Research (PTK) approach, which is carried out in two cycles. Each cycle consists of four stages, namely planning, implementation of actions, observation, and reflection. The subjects in this study were 7 children aged 4–5 years who were members of Group A. Data collection techniques included observation, documentation, and performance assessment. The results of the study showed a significant increase in children's ability to recognize hijaiyah letters. In the initial condition (pre-action), only 28.57% of children were in the Developing According to Expectations (BSH) category. This percentage increased to 71.43% in cycle I, and reached 85.7% in cycle II. These findings prove that the Tilawati method is effectively used in improving the recognition of hijaiyah letters in early childhood, especially in the context of Islamic religious learning in kindergarten.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".