Clustering Menggunakan Algoritma K-Means untuk Mengelompokan Data Perjudian Berdasarkan Wilayah di Kota Binjai (Studi Kasus : Pengadilan Negeri Binjai)
Bibliographic record
Abstract
The Binjai District Court is a government agency that has the duty and authority to receive, examine and decide every case registered at the Binjai District Court. The Binjai District Court handles many gambling cases, but data management is still not fast and accurate because it still uses manual methods, so the agency needs to implement an application system. To solve this problem, you can use data mining applications, namely by utilizing existing data to dig up new information. One of the techniques in data mining is clustering. Clustering was chosen because it can group data according to the desired characteristics, in this research it means grouping gambling data in the Binjai City area. The clustering algorithm used is K-Means Clustering integrated into a desktop-based programming application. The conclusion obtained is that the system designed has proven successful in grouping gambling data into 3 clusters (groups). The process using MATLAB R2014a obtained results in group 1 which amounted to 276 data with a data centroid center (6.92; 2.41; 4.33) including the category of low levels of gambling, group 2 which amounted to 337 data with a data centroid center (7.56 ; 2.10; 14.48) is included in the category of moderate level of gambling and group 3 which amounts to 387 data with the centroid data (7.56; 2.10; 28.02) is included in the category of high level of gambling.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".