Application of the K-Means Method for Clustering Land and Building Tax Payments Based on Tax Types (Case Study: BPKPAD Binjai City)
Bibliographic record
Abstract
Land and Building Tax or abbreviated as PBB is a fee that must be paid for the existence of land and buildings owned by the community or residents. The determination of PBB in Binjai City is based on the application of the Land Value Zone (ZNT) which is close to the market price, which will be able to create equitable development throughout Binjai City. BPKPAD (Regional Revenue and Assets Financial Management Agency) Binjai City is a government agency that receives PBB payments from the community. Data - data on PBB payments for the people of Binjai City have been stored in an existing system and every year it will continue to increase so that it will cause data accumulation in the land and building tax archives. A data processing system is needed to manage these data, one of which can be done with data mining which can process piles of data into useful information and can be utilized by grouping PBB data based on criteria. Clustering is a method in data mining that can be used to automatically detect clusters of adjacent records that have a certain definition in all variables. K-Means algorithm is a simple algorithm to classify or group a large number of objects with certain attributes into groups (clusters). So that this system can be used as input for the Binjai City BPKPAD in finding solutions to increase regional income from PBB payments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".