PENGELOMPOKAN DATA KELUHAN PASIEN PADA LAYANAN RUMAH SAKIT BERDASARKAN KATEGORI MASALAH MENGGUNAKAN METODE CLUSTERING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Penelitian ini membahas pengelompokan data keluhan pasien pada layanan RSU Artha Medica Binjai berdasarkan kategori masalah menggunakan algoritma K-Means Clustering. Data penelitian mencakup periode 2023–2024 dengan variabel umur pasien, kategori keluhan, dan kategori masalah. Proses pengolahan dilakukan menggunakan perangkat lunak Matlab R2014a, menghasilkan enam cluster dengan karakteristik berbeda. Hasil pengujian menunjukkan konfigurasi enam cluster memiliki nilai cluster variance terendah sebesar 4,5682, menandakan distribusi data paling kompak dibanding konfigurasi lainnya. Secara khusus, cluster keenam memiliki variance 5,0008 dengan Vmin 0,2472 dan Vmaks 10,3912, menunjukkan variasi yang terkendali dan sebaran data yang merapat ke pusat cluster. Temuan ini membuktikan bahwa penerapan K-Means Clustering dapat membantu rumah sakit dalam memahami pola keluhan pasien secara lebih akurat dan menjadi acuan strategis untuk peningkatan kualitas pelayanan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.048 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it