Pengelompokkan Pendonor Darah Berdasarkan Golongan Darah Di PMI Kabupaten Langkat Menggunakan Metode Clustering K-Means
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.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".