FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN BERINVESTASI DI PLATFORM PEER TO PEER LENDING DI KOTA PADANG
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh persepsi resiko, jaminan struktural, persepsi reputasi, dan pengetahuan P2PL terhadap keputusan investasi di platform P2PL. Data penelitian diperoleh langsung oleh peneliti dari penyebaran kuesioner kepada 100 responden. Metode pengambilan sampel dalam penelitian ini ialah metode purpose sampling. Pengolahan data dalam penelitian ini menggunakan metode analisis regresi linear berganda dengan alat bantu software SPSS. Hasil penelitian menunjukkan bahwa Persepsi Resiko, Jaminan Struktural, Persepsi Reputasi berpengaruh signifikan terhadap keputusan investasi di platform P2PL. Sedangkan variabel Pengetahuan P2PL tidak berpengaruh terhadap keputusan investasi P2PL. \n \n \n \nKata Kunci: Peer to Peer Lending, Persepsi Resiko, Jaminan Struktural, Persepsi Reputasi, Keputusan investasi
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".