Upaya Peningkatan Kemampuan Motorik Halus Dengan Mengurutkan Bentuk Geometri Melalui Kegiatan Meronce (Siswa Sswi Kelompok A RA Daruttaqwa Ponggok)
Bibliographic record
Abstract
This study aims to find out the application of meronce activities by sorting geometric shapes in improving fine motor skills, where the activity of sorting geometric shapes previously did not use media or only by working on student worksheets which were considered less efficient. The activity of sorting geometric shapes by tracing geometric shapes can give an interesting impression to children because they don’t feel bored with these activities and they feel happy. Researchers use the activity of tracing geometric shapes to improve fine motor skills by sequencing with the aim that children become more understanding and children don’t get bored while learning. The method used in this study is Classroom Action Research with the Kurt Lewin model. This study consisted of pre-cycle, cycle I, cycle II and cycle III. In cycle I, cycle II and cycle III consists of 4 stages, namely planning, implementation, observation and reflection. Data collection techniques in this study used observation, interviews, non-test assessments and documentation in the form of photos. The analysis technique in this study uses a qualitative description analysis. The results of this study are as follows: 1) The ability to sort geometric shapes through beading activities at RA Daruttaqwa Ponggok obtained satisfactory results. This can be seen from the results of observations of children’s activities in the first cycle of 64.70 (enough), the second cycle of 85.29 (good) and in the third cycle increased to 89.70 (very good). This is all because researchers and teachers can pay attention to every ability that is in each student, so that all students get the same teaching with various forms of assignment techniques. 2) There is an increase in the ability to sort after the implementation of the activity of tracing geometric shapes can be seen from the results of student learning completeness in that the ability to sort sequences develops very well. This can be seen from the average value showing the results of the first cycle of 50.47 (Starting to Develop), in the second cycle of 71.54 (Developing According to Expectations) and in the third cycle of 85.26 (Developing According to Expectations) and has fulfilled the establishe
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".