Pemamfaatan Metode Clustering Pada Nasabah Peminjaman Modal (Studi Kasus: PT. Faderal International Finance Binjai)
Bibliographic record
Abstract
Peminjaman modal merupakan transaksi tersepakati dari dua belah pihak bermaksud meminjan uang/dana kepada seseorang atau badan usaha peminjaman. PT. Faderal International Finance Binjai sebagai salah satu satu badan usaha yang bergerak di bidang keuangan atau jasa keuangan yang menyediakan peminjaman modal dengan menjaminkan surat berhaga sebagai penjamin Dalam rekapitulasi data nasabah dalam pengelompokkan nasabah untuk mengetahui jumlah nasabah dalam peminjaman modal sering dilakukan secara komputerisasi bahkan manual yang mengakibatkan sulit dalam pengelompokkan dan mengetahui jumlah nasabah. Pada penelitian ini dalam Pemamfaatan Pada Nasabah Peminjaman Modal menggunakan metode clustering dalam nilai yang di hasilkan menggunakan program mendapatkan hasil yang berbeda - beda pada penggunaan cluster 2 dan cluster 3. maka dapat di simpulkan penggunaan metode clustering mampu mengelompokkan data nasabah peminjaman modal di PT. Faderal International Finance Binjai.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.024 |
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".