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Edwin Setiawan Nugraha
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Kota Jababeka, Cikarang, Kabupaten Bekasi, Jawa Barat
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INDONESIA
Journal of Actuarial, Finance, and Risk Managment
Published by President University
ISSN : -     EISSN : 28303938     DOI : -
Core Subject : Economy, Education,
Economics, Econometrics & Finance Mathematics
This journal aims to provide high quality articles covering any and all aspects of the most recent and significant developments in the actuarial, financial, and risk management.
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1 (2022)" : 5 Documents clear
Logistic Regression Analysis of Demographic and Vehicle Condition for Purchasing Vehicle Insurance Gabriel Azhar; Muhammad Cahirul Rahman; Rosyid Nur Salam; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3673

Abstract

Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection or reimbursement against losses from an insurance company. Insurance policies are used to hedge against the risk of financial losses, both big and small, that may result from damage to the insured or her property, or from liability for damage or injury caused to a third party. Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue. In this research, we use the secondary data that collected in India in 2020, which analyzes vehicle condition, demographics, and owning a driver’s license on vehicle insurance buying interest. The method used in this research is the Logistic Regression, the response variable is the Response (of buying vehicle insurance interest), and the independent variables are Gender, Driving License, Previously Insured, Vehicle Age, and Vehicle Damage. The result of this research showed that the Previously Insured, Vehicle Age, and Vehicle Damage have a correlation to the Response.
Log Linear Model on Contingency Table to Analyze Relationship between Age, Income, and Health Insurance Ownership Evelyn Priscilla; Jeslyn Prinssesa; Mei Siang Jemima Aurelia; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3674

Abstract

Health insurance is a type of insurance that is important for everyone to have since it has benefits as protection against health risks that may occur in the future. Unfortunately, most people nowadays do not really want health insurance, especially people who are relatively young and have low incomes. Young people feel that they are still strong and do not get sick easily, while people with low incomes cannot afford to buy insurance because of the high premium prices. Therefore, the relationship between age, income, and insurance ownership (other than BPJS) needs to be known to help insurance companies develop new strategies. In this study, we implemented a Log-Linear model on a contingency table using survey data that we took in Jabodetabek, Bali, and Kalimantan areas. The results showed that the Log-Linear model (OI.OA.IA) was efficient enough to determine the relationship between age, income, and insurance ownership with a 95% confidence level. Homogeneous interactions happened so that there is no relationship between age, income, and insurance ownership, but there were relationships between age and income, age and insurance ownership, and income and insurance ownership. This research is expected to assist insurance companies in determining their target market and developing their marketing.
ARIMA Model in Predicting Jakarta Composite Index Shafa Luthfia Sari Haerani; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3675

Abstract

This study discusses stock price modeling using ARIMA model. We apply to model to the Jakarta Composite Index (JCI) as it represents all stock performances listed in Indonesia Stock Exchange. In this study, we propose several ARIMA models based on the daily from June 10th, 2019 until December 6th, 2019. The parameters among the models are estimated by using RStudio. We chose the best model by considering its AIC and RMSE. The best model that is ARIMA (21, 1, 2) with 99% confidence interval. This model is then used to predict the next 15 days (December 09, 2019 to January 02, 2020).
Online Shopping Website Analysis for Marketing Strategy Using Clickstream Data and Extra Trees Classifier Algorithm Diah Prastiwi
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3676

Abstract

On an online shopping website, the platform may provide a service to the shop owners by suggesting which items to promote. One possible consideration is price. If an item is priced more expensively than the average price of other items in the same category, then the item should be advertised more intensely, or repriced. Due to the quickly growing number of products and categories, calculating the average price in real time can be difficult or slow. Alternatively, one may employ machine learning algorithms. In this study, we use Extra Trees Classifier on clickstream data, which is user activity report. We demonstrate the algorithm on the clickstream data of a an online shopping website for pregnant women, retrieved from UCI Machine Learning Repository Dataset. The data has 14 attributes and 165474 entries. The model is trained on 75% of the data, and tested on the remaining 25%, with an observed accuracy of 99 %.
Forecasting of Retirement Insurance Filled via Internet by ARIMA Models Lovena Louisa; Rifky Fauzi; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3672

Abstract

Pension fund insurance is critical for everyone because it can guarantee a good life during retirement because retirement is a period when someone no longer gets a steady income. Technological advances make it easier for retirement insurance applications. By using ARIMA Models, we can predict the number of internet users who apply for retirement insurance via the internet, using the monthly data of the Social Security Administration from January 2008 to October 2020. The data used has a steady increasing trend with some seasonal components, so it needs to be removed first. ARIMA models use the assumption that the data is stationary, so the data must be tested using the ADF test command in R. After seeing the plotting of ACF and PACF, 9 ARIMA models are formed. ARIMA model is selected based on the smallest AIC. By using 95% confidence it can be concluded that ARIMA (9,1,9) is the best model for forecasting.

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