PEMILIHAN TEMPAT PENYULUHAN BERDASARKAN TINGKAT KERAWANAN DENGAN MENGGUNAKAN METODE SMART
Bibliographic record
Abstract
The National Narcotics Agency is a non-structural institution tasked with coordinating relevant government agencies in the formulation of policies and their implementation in the field of availability, prevention, eradication, abuse and illicit trafficking of narcotics (P4GN). In an effort to contain the rate of increase in the prevalence of drug abuse and illicit trafficking in Binjai City, the National Narcotics Agency of Binjai City conducts anti-drug counseling activities to the entire community. In this study, the SMART (Simple Multi Attribute Rating Technique) method was used to build a web-based system that aims to make it easier to determine the right place for anti-drug counseling. The results of this study indicate that by applying the method (Simple Multi Attribute Rating Technique) the best alternative can be obtained, namely A5 (Tunggorono Village) with the criteria values: C1 (Number of Drug Crime Cases) = 0.3; C2 (Crime Rate) = 0.25; C3 (Narcotics Use Rate) = 0.20; C4 (Drug Location) = 0.15; C5 (Low Social Interaction) = 0.1 with a total final result of 1.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".