Application of Apriori Algorithm to Find Patterns of Population Mortality Data (Case Study: Disdukcapil Stabat)
Bibliographic record
Abstract
The development of information technology provides great opportunities in data utilization, including in government agencies. One of the important data managed by the Population and Civil Registration Office (Disdukcapil) is population mortality data. This data not only serves as an administrative archive, but can also be analyzed to identify important patterns related to the factors causing death. This study aims to apply the Apriori algorithm in identifying association patterns from population death data based on factors such as age, gender, occupation, cause of death, and address at the Disdukcapil Stabat. The method used is data mining with the Apriori algorithm, through the stages of data processing, determining the support, confidence, and lift values until a rule is formed. The results of the study show that 173 association rules were formed, with the best rule having the highest support value of 6% and confidence of 10%. The rule states that if the age of the population is over 56 years with an address in Stabat, then the tendency of gender is male, occupation as an entrepreneur, and sudden death.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".