Clustering Data On Underage Marriage Using The Clustering Method
Bibliographic record
Abstract
In Law no. 1 of 1974, article 7 paragraph (1) states that marriage is only permitted if the man has reached the age of 19 and the woman has reached the age of 16. Nationally, early marriage to the age of under 16 is 26.95%. In fact, based on the findings of Bappenas in 2008, it was stated that 34.5% of the 2,049,000 marriages in 2008 until now were child marriages which were increasing rapidly (Rifiani, 2011: 126). The influence of foreign culture is also one of the causes of the large number of underage marriages, foreign cultures which are very famous for freedom of dating, are the views of today's youth to have relations outside of legal marriage. Not only culture, information technology in the 4.0 era has greatly influenced the occurrence of underage marriages, adult video sites that are easily accessible via the internet. For this reason, the K-means clustering method is used as the right solution for the problem of underage marriage data by grouping the data based on age, gender, and occupation to get definite data, so that data grouping using the applicationmatlab andrapid miner can produce output from data mining that can be used in making decisions in the future Keywords: Underage marriage, clustering, matlab
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".