Pemamfaatan Metode Clustering Pada Nasabah Peminjaman Modal (Studi Kasus: PT. Faderal International Finance Binjai)
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.006 |
| 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