Data Mining Grouping Of Drug Users By Age Using Clustering Method (Case Study: BNN Binjai City)
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
Drug trafficking and abuse is a very complex problem, which requires efforts to overcome it. Given that there are still many obstacles in the process of grouping drug users at the Binjai City BNN Office, for this reason the author tries to create a system to support a computerized grouping process that can help automatically classify drug users based on age, so there is an opportunity to design a grouping data mining system in it. Data mining is part of a computer-based information system that employs one or more computer learning techniques to analyze and extract knowledge automatically that is used to support grouping within an organization or a company. Clustering is a method that is applied to create a grouping data mining system to make it easier for staff to classify drug users based on age. Based on the analysis that has been done on grouping drug user data using the clustering, it is necessary to do the cluster several times to get the same results according to the first process. In this process, the process is carried out 10 times to obtain cluster. In cluster 1 which is 3 9 4, cluster 2 is 3 1 4, cluster 3 is 3 5 4 with the number of members of cluster 1 as much as 322 data, cluster 2 as much as 81 data and cluster 3 as much as 97 data.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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