Data Mining Pengelompokan Pasien Rawat Inap Berdasarkan Kelas Bpjs Menggunakan Metode Clustering (Studi Kasus : Rumah Sakit Umum Daerah Dr. Rm. Djoelham Binjai)
Bibliographic record
Abstract
RSUD Dr. R.M. Djoelham Kota Binjai merupakan lembaga penyedia jasa layanan kesehatan berdiri sejak tahun 1927 di kota Binjai yang menyediakan pelayanan rawat inap bagi pasien yang sedang sakit, kecelakaan maupun pemulihan kondisi (pasca operasi). RSUD Dr. R.M. Djoelham memberikan pelayanan rawat inap yang baik, dari segi pelayanan yang diberikan perawat, pelayanan medis, pelayanan kamar, maupun fasilitas lainnya. BPJS kesehatan membantu ketersediaan untuk semua kebutuhan biaya dokter, obat-obatan, rawat inap, sampai dengan tindakan operasi. Pengelompokkan pasien rawat inap berdasarkan kelas BPJS menjadi hal yang penting pada database rumah sakit terdiri dari banyak kelas BPJS yang digunakan dalam kegiatan rawat inap rumah sakit. Namun, dalam kegiatan ini masih susah untuk didentifikasikan karena disetiap harinya banyak pasien masuk. Teknik data mining dapat menggali data kasus yang berjumlah besar dan menghasilkan informasi tentang pengelompokkan pasien rawat inap berdasarkan kelas BPJS sesuai dengan clustering masing-masing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".