Penerapan Hukum De Moivre Untuk Menentukan Nilai Penyisihan Kontribusi Asuransi Jiwa Dwiguna Dengan Metode Canadian
Bibliographic record
Abstract
This research examines the application of De Moivre's Law in determining the value of the contribution allowance for endowment life insurance using the Canadian method. Contribution allowance is very important for life insurance companies to meet claims arising in the current or future period. Insufficient contribution allowance can lead to financial risks, especially when claims submitted exceed predictions. This research aims to develop a contribution allowance calculation model using De Moivre's Law in the Canadian method, compare the results with calculations without using De Moivre's Law, and assess the financial implications for insurance companies. Data was taken from the Indonesian Mortality Table IV and processed using Microsoft Excel. The research results show that the Canadian method without De Moivre's Law produces a greater contribution allowance value in the early years, which provides financial benefits for the company.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".