Sistem Informasi Monitoring Data Obat di Puskesmas Bontoperak Kabupaten Pangkajene dan kepulauan
Bibliographic record
Abstract
This research aims to design and create applications that can monitor drug data, namely incoming drug data, outgoing drugs, drug stocks and report making. The research method used is R&D because researchers want to produce products and test the effectiveness of the products in order to function. While the pngujian method on this system uses black box testing which aims to perform tests based on system details such as the appearance, functions and suitability of the function flow of the system. From the results of system testing, all functions in the system can function properly so as to help the performance of the officers in the health center, especially those who handle drug data. The drug data monitoring information system has been successfully designed to be used by the Head of Puskesmas, warehouse officers, and pharmacy officers. Drug warehouse officers are given the facility to enter data on sources of funds, suppliers, drugs and conduct drug transactions in and out of the warehouse. Furthermore, pharmacy officers can enter patient data and make drug transactions out at the pharmacy. As for the Head of puskesmas has facilities to be able to see the report and then print it.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".