Penerapan Metode Apriori Pada Data Penduduk Berdasarkan Tingkat Kesejahteraan (Studi Kasus : Kantor Camat Sirapit)
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 Indonesian government has implemented various programs to improve public welfare; however, social assistance often misses its target, primarily due to a lack of accurate data. Sirapit Subdistrict, as a government institution, has access to important population data for policy development, particularly in the distribution of aid based on community welfare levels. Factors such as education, age, number of dependents, and income play a significant role in determining an individual's welfare. To address this issue, this study proposes the use of the Apriori method to analyze the factors affecting population welfare. The Apriori method is a data mining algorithm useful for discovering association patterns within a dataset. The study results show that with a support value of 3% and a confidence level of 100%, a pattern was found where residents with a high school education, 1-2 dependents, aged 35-45 years, earning Rp 500,000 - Rp 999,999, and with a low welfare level tend to work as laborers. These findings are expected to serve as a foundation for formulating more targeted policies to improve community welfare in Sirapit Subdistrict.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it