8. SEGMENTASI TINGGI BADAN DAN BERAT BADAN KADET MAHASISWA MENGGUNAKAN K-MEANS 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 merupakan langkah awal yang digunakan sebagai syarat utamaserta bekal dan persiapan dalam rangka menentukan Kadet mahasiwa UNHAN RI yangakan bekerja pada instansi pertahanan yang memiliki pengetahuan akademik dan militer.Penelitian ini bertujuan untuk segmentasi kadet mahasiwa berdasarkan tinggi badan danberat badan yang nantinya akan membantu pembuat keputusan dalam hal pembinaan fisik.Untuk segmentasi kadet mahasiswa ini peneliti menggunakan metode K-Means Clustering.Dari hasil segmnetasi didapat tiga cluster yaitu cluster 0, cluster 1, dan cluster 2. Cluster 0menunjukkan adanya potensi untuk dilakukan pembinaan lebih lanjut, sedangkan cluster 1dan cluster 2 juga bisa dilakukan pembinaan tapi dengan level sedang dan sederhana
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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