Family Economic Correlation To Students Learning Achievment Using Apriori Method
Bibliographic record
Abstract
The education system in Indonesia as mandated in the GBHN aims to educate the nation while at the same time responding to new challenges to create a decent and prosperous life. Understanding, apprecation, and experience of cultural and religious values in the right and true form will be increasingly needed. The economic status of the family is one of the factors that is sufficient to support the level of continuing education, especially for teenagers who are still student in school. Apriori method is used to obtain association rules that describe the relationship between item in the transactional database. There are two databases used, each of which has a different number of transactions. This study aims to aplly the apriori algorithm, as an analytical technique. The data taken as a case example is familiy economic data. This association search uses WEKA which will later find the rules and MySQL as the placeholder for the Database. From the results of the analysis using apriori, the highest confidence value was obtained at 0.9 with support 0.1 resulting in a students rule whose economics supported the learning achievement was very supportive, and the lowest confidence value of 0.2 with support 0.1 resulted in a students rule who had sufficient economics, so their learning achievement was also quite increased..
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".