Identifikasi Lokasi Pemusatan Fasilitas Rumah Sakit Kota di Surakarta
Bibliographic record
Abstract
Health is the optimal general state of one's body, mind, and spirit. Someone can do an activity because there is a healthy body so in general everyone always tries to stay healthy. The availability and convenience of health facilities is one of the efforts made by the government to improve the quality of public health. The purpose of this study with the Mean center method and Density estimation analysis with Heatmap, is to gain a better understanding of data distribution patterns. The Mean center method is used to determine the central point of distribution of data which can then be used as a reference to analyze and understand further distribution patterns. From this analysis it can be concluded that the Mean center method can help in identifying the center point or midpoint in the Surakarta area, describing the ideal location in Surakarta City, this method allows geographical representation of the center point using geographical coordinates such as latitude and longtitude which can then provide an easy understanding of the relative location of the center point of the hospital to other hospitals in the region. The results of the Mean center show that the center point of the hospital is located in Kelurahan Kestalan, Kecamatan, Banjarsari Surakarta City. Density estimation results in 3 (three) concentration clusters in Surakarta City. This helps local governments in plans to add health facilities, because of the gap in the northern, central and southern regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".