ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MOTIVASI, PERSEPSI DAN SIKAP NASABAH TERHADAP KEPUTUSAN PEMBELIAN PRODUK ASURANSI PENDIDIKAN
Bibliographic record
Abstract
ABSTRAKTujuan penelitian ini adalah untuk menganalisis signifikansi pengaruh motivasi, persepsi dan sikap secara parsial dan simultan terhadap keputusan pembelian produk asuransi pendidikan Sequislife di Solo. Penelitian ini di lakukan dengan metode kuesioner terhadap 75 responden nasabah asuransi Sequislife di Solo yang diperoleh dengan menggunakan teknik pengambilan sampel yaitu non probability sampling dengan jenis yang di gunakan adalah purposive sampling. Kemudian dilakukan analisis terhadap data- data yang di peroleh berupa analisis kualitatif dan kuantitatif, uji asumsi klasik, regresi linier berganda, uji t, uji F dan uji koefisien deteminasi (R2). Penelitian ini menghasilkan persamaan regresi Y = 5,006 + 0,330X1 + 0,204X2 + 0,252X3. Berdasarkan analisis tersebut dapat di ketahui bahwa motivasi, persepsi dan sikap nasabah berpengaruh positif terhadap keputusan pembelian produk asuransi pendidikan Sequislife di Solo. Hasil uji t menunjukkan bahwa motivasi, persepsi dan sikap nasabah secara parsial berpengaruh signifikan terhadap keputusan pembelian produk asuransi pendidikan Sequislife di Solo. Hasil uji secara serempak (uji F) dengan signifikansi 0,000 < 0,05 sehingga diperoleh hasil motivasi, persepsi dan sikap nasabah secara simultan berpengaruh terhadap keputusan pembelian produk asuransi pendidikan Sequislife di Solo. Hasil uji koefisien determinasi (R2) didapatkan variabel motivasi, persepsi dan sikap nasabah memberikan kontribusi sebesar 62,6% terhadap keputusan pembelian produk asuransi pendidikan Sequislife di Solo sedangkan sisanya 37,4% di pengaruhi oleh faktor lain diluar variabel yang di teliti.Kata kunci : motivasi, persepsi, sikap dan keputusan pembelian
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".