Application of Apriori Algorithm to Find Patterns of Population Mortality Data (Case Study: Disdukcapil Stabat)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it