Pengelompokan Data Penerima Bantuan untuk Disabilitas di Kota Binjai Menggunakan Metode Clustering Algoritma K-Means
Why this work is in the frame
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Bibliographic record
Abstract
In Indonesia, people with disabilities are often overlooked and underestimated because they do not have perfect physical abilities to do certain jobs or activities. The majority of them come from underprivileged families and are often underdeveloped. The unstructured process of distributing assistance can result in the assistance provided is not in accordance with the needs, so it is not optimal in improving the welfare of persons with disabilities. In addition, without a clear grouping, it is difficult for the government to design a more specific and targeted assistance program. Therefore, to overcome this problem, the agency needs to have an additional system to be able to assist in overcoming the problem of disability assistance recipients, namely by using the clustering method to group beneficiary data based on age, type of disability, and type of assistance. Thus, this clustering is expected to provide information and a clearer picture of the needs of each disability group, so that the assistance program provided can be distributed more optimally according to what people with disabilities need. After calculating using the existing cluster formula4, iteration 2 is the same as in iteration 1 and there is no data that moves groups anymore so the calculation can be stopped. So that a cluster graph can be made grouping data on beneficiaries of assistance for disabilities in Binjai City using the K-Means algorithm clustering method.
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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