Grouping Data of Patients Who Are Conducting Drugs Abuse Rehabilitation Using The Clustering Method (Case Study: BNNK Binjai)
Bibliographic record
Abstract
Rehabilitation is an appropriate alternative punishment for drug addicts. By utilizing data mining using input data in the form of rehabilitation patient data at BNNK Binjai, the data will be processed using the clustering method using the k-means algorithm. K-Means is a non-hierarchical data clustering method that seeks to partition existing data into one or more clusters or groups so that data has characteristics. Of the 20 data tested in cluster 1 there are a total of 13 data and are located in the Age group (X) which is 26-35 years old, and for the substance type group (Y) used is methamphetamine and in the Occupational group (Z), namely Self-employed. in cluster 2 there is a total of 5 data and it is located in the Age group (X) which is 26-35 years old, and for the Substance type group (Y) used is Shabu and in the Employment group (Z) namely Not Yet Working. in cluster 3 there is a total of 2 data and it is located in the Age group (X) which is 26-35 years old, and for the Substance type group (Y) used is Shabu and in the Occupational group (Z) namely Private Employees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".