Penerapan Metode Monte Carlo pada Simulasi Antrian Poliklinik RSUD DR. RM. Djoelham
Bibliographic record
Abstract
Long queues at the polyclinic of RSUD RM DR Djoelham Binjai often cause inconvenience to patients and reduce service efficiency. This study aims to analyze the queuing system at the hospital's polyclinic using the Monte Carlo method, which is able to model uncertainty in patient arrivals and service times. With this method, it is expected that a more accurate picture of patient waiting time and queue performance can be obtained so that improvement measures can be identified. The data used in this simulation includes the number of patients who come and the service time in the polyclinic. Monte Carlo simulations are carried out to predict various queuing scenarios based on variations that occur in patient arrivals and service duration. The simulation results provide information related to the estimated average waiting time of patients, and the level of queue density. This study shows that the application of the Monte Carlo method is effective in providing a more measurable solution to minimize waiting time and improve service quality at the polyclinic of RM DR Djoelham Binjai Hospital. These results are expected to be a reference for hospital management in making strategic decisions related to the optimization of health services. With the average waiting time for patients in the queue is 10.59 minutes while the average patient time is 25.34 minutes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".