KAJIAN CADANGAN ASURANSI DENGAN \nMETODE ZILLMER DAN METODE KANADA
Bibliographic record
Abstract
Abstrak \n \nCadangan asuransi adalah sejumlah uang yang harus disediakan oleh pihak \nperusahaan asuransi dalam waktu pertanggungan dan digunakan untuk membayar \nsantunan sesuai dengan kesepakatan pada awal kontrak. Cadangan akan \ndisesuaikan berdasarkan preminya menjadi beberapa metode. Metode yang \ndigunakan dalam skripsi ini adalah Metode Zillmer dan Metode Kanada. \nPenelitian ini bertujuan untuk menentukan formulasi cadangan pada produk \nasuransi jiwa dengan mengkaji metode Zillmer dan Kanada secara teoritis dan \nmelakukan simulasi terhadap konsep cadangan pada asuransi jiwa perorangan \nbiasa (ordinary insurance) yang meliputi Asuransi Berjangka, Asuransi \nEndowment Murni, Asuransi Dwiguna, dan Asuransi Seumur Hidup. Langkahlangkah yang digunakan untuk mengkaji nilai cadangan tersebut adalah : (1). \nMenentukan nilai APV (Actual Present Value), (2). Menentukan nilai anuitas, \n(3). Menentukan nilai premi, (4) Menentukan nilai cadangan Netto, (5) \nMenentukan nilai cadangan metode Zillmer dan metode Kanada sebagai akibat \nadanya faktor biaya pada premi. Berdasarkan hasil penelitian, penulis \nmenyimpulkan metode Kanada cocok untuk jenis asuransi Endowment Murni dan \nDwiguna sementara metode Zillmer hampir di semua jenis asuransi dapat \ndigunakan. \nKata kunci : Cadangan, Metode Zillmer, Metode Kanada. \n \n \n \n \n \n \n \n \n \nAbstract \n \nReserve insurance is amount of money that must be provided by the insurance company \nin time account and used to pay compensation in compliance with agreement at the \nbeginning of the contract. Reserve will be adjusted based on the premi into several \nmethods. Methods which is used in this thesis is Zillmer and Canada methods. This \nresearch aimed at determinig formulations of reserve for life-insurance products \ntheoretically with assessing Zillmer and Canada methods and doing simulation to \nthe cencepts of reserves individual ordinary life insurance (ordinary insurance) \nwhich include periodically insurance, Pure Endowment Insurance, Dwiguna \nInsurance, and Lifetime Insurance. The measures used to assess the value of these \nreserves are : (1) determine the value of APV (Actual Present Value), (2) \nDetermine the value of the annuity, (3). Determine the value of premi, (4) \ndetermine the value of net reserves, (5) determine the value of reserves using \nZilmer and Canada Method as a result of the cost factor of premi. Based on the \nresults of the study, the authors conclude Canada methods suitable for Pure Endowment \nInsurance and Dwiguna Insurance while the Zillmer method almost can be used in all \ntypes of insurance. \nKeywords : Reserve, Zillmer Method, Canada Method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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".