Searching For The Nearest Route To The Location Of Health Facilities Using The Djikstra Method
Bibliographic record
Abstract
A health facility or health service facility is a tool or place used to carry out health service efforts, both in terms of promotive, preventive, curative and rehabilitative carried out by the central government, regional government or the community. This research contains development applications that cover every hospital, health center, and practice clinic located in the Langkat Regency area with the aim of facilitating the community in finding the nearest hospital, health center, and practice clinic. The application built can display the location of the health facility in map form and can display information in the form of name, address, telephone number, photo of the health facility, services available there, working hours, and further information on the place. In this study, the search for health facilities is only subject to distance as a health facility criterion, so that it can be developed further. To find the closest route to a health facility, here the author uses the Dijkstra Algorithm method which has been widely researched to be applied to the shortest route search system. This algorithm was invented by Edsger Dijkstra, a computer scientist from the Netherlands. The way Dijkstra's algorithm works is with a greedy strategy, namely at each step it chooses the side with the smallest value that connects the selected and unselected nodes/nodes. This algorithm requires a point of origin and a destination with the final result being the shortest distance from the point of origin to the destination along with the route.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".