ANALISIS POTENSI PENERIMAAN RETRIBUSI PELAYANAN PARKIR DI TEPI JALAN UMUM KOTA BANJARMASIN
Bibliographic record
Abstract
The purpose of this study was to see the actual potential of revenue from the retribution of parkirng services on the edge of the public roads of Banjarmasin City in 2018. The type of research used is descriptive statistics, with a population of 208 parking retribution points in 5 sub-districts of Banjarmasin City and using the Slovin test to determine 22 samples. The data used is primary data obtained from field observations. The analysis techinque used is selecting sampling and standard deviation techniques. Selecting sampling by observasing for 10 minutes parking reception at busy and quiet hours on weekdays and weekends. While the standard deviation is used to see the upper and lower limits of potensial retribution for parking service on the edge of public roads.The results obtained in this study show that the target set is able to cross the lower standard deviation, which means that the performance of the local government, especially the transportation agency, is quite good, but the revenue from the on-street parking retribution can be improved. For the North Banjarmasin region it can be increased by 101%, then South Banjarmasin by 218%, then for the West Banjarmasin region by 26%, then for East Banjarmasin by 26% and the Central Banjarmasin area by 23%. Whereas for the the centra antasari market, it can be increased by 29%Keyword: Potential, selecting sampling, parking retribution.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".