Penerapan Metode Apriori untuk Mengidentifikasi Korelasi Nilai Siswa di Sekolah Menengah Pertama
Bibliographic record
Abstract
Currently, education is developing very rapidly, starting from the school level even up to the university level. Quality human resources depend on education. Student learning achievement which is usually indicated by report card grades is one of the benchmarks of educational success. However, student grade data for strategic decision making in schools is often done manually and is not optimal. This study aims to identify correlations between student grades in Junior High Schools and apply the Apriori method in data analysis to determine the relationship between student grades and other variables such as subjects and achievement levels. This study involved data collection consisting of 508 students. The Apriori method successfully identified relevant correlations, such as students in Pancasila Education subjects received a B, ICT received a B, Mathematics received a B, English received a B, Social Studies received a B, Craft received a B, Indonesian received a B, Physical Education received a B, Science received a B, SBK received a B, Religious Education received a B, then the Achievement Level is Low with support 3.90% and confidence 95.20%. The use of RapidMiner software in data analysis provides recommendations for robust relationships or correlations. This research is expected to provide sound recommendations to support the achievement of national education goals by identifying the relationship between student grades and subject matter, as well as improving learning outcomes based on student achievement levels.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".