Pengelompokkan Pendonor Darah Berdasarkan Golongan Darah Di PMI Kabupaten Langkat Menggunakan Metode Clustering K-Means
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 bertujuan untuk mengelompokkan data pendonor darah berdasarkan golongan darah, tempat domisili, dan usia menggunakan metode Clustering K-Means. Palang Merah Indonesia (PMI) Kabupaten Langkat menghadapi tantangan dalam pengelolaan data pendonor yang masih bersifat konvensional, sehingga diperlukan pendekatan berbasis data mining untuk meningkatkan efisiensi dan akurasi pengelompokan data. Data sebanyak 2000 pendonor diolah menggunakan algoritma K-Means melalui perangkat lunak MATLAB R2014b dengan konfigurasi 3, 4, dan 5 cluster. Hasil pengujian menunjukkan bahwa konfigurasi 5 cluster memiliki nilai rata-rata variance terendah sebesar 2,2048, yang menandakan bahwa pengelompokan lebih kompak dan stabil dibandingkan konfigurasi lainnya. Mayoritas pendonor darah berasal dari golongan darah A, domisili Kecamatan Stabat, dan berusia 25–44 tahun (dewasa). Pengelompokan ini diharapkan dapat membantu PMI Kabupaten Langkat dalam menyusun strategi pelayanan donor darah secara lebih tepat sasaran dan efisien.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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