Penggunaan Metode Rough Set Pada Pola Minat Dan Bakat Siswa Dalam Menentukan Tema P5
Bibliographic record
Abstract
This research aims to identify the patterns of students' interests and talents at Esa Prakarsa Junior High School and apply the Rough Set method in data analysis to determine the most appropriate theme of the Pancasila Student Profile Strengthening Project (P5). The study involved data collection from 178 students through a questionnaire designed to explore their interests and talents. The results of the analysis showed a significant correlation between the patterns of interest and talents of students with the selection of the P5 theme. The Rough Set method successfully identified relevant rules, such as students who have an interest in the field of art are more suitable for the theme of sustainable lifestyle, while talented students in the field of sports are more in line with the theme of Build the Soul and Body. The use of Rosetta software in data analysis provides recommendations for interesting and relevant P5 themes, supporting the achievement of national education goals in forming a young generation with character and competence. This research is expected to provide guidance for schools in developing P5 themes that are more relevant and interesting for students, as well as improving learning outcomes based on their interest and talent characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".