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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
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Search results for , issue "Vol 1, No 2 (2022)" : 5 Documents clear
Comparison of Premium Reserves with New Jersey Methods and Full Preliminary Term on Endowment Insurance Filemon Febrian Bintoro; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3969

Abstract

Life insurance companies often have difficulty getting fees at the beginning of the insurance year. It’s noted that there are several life insurers who suffer losses caused by the inability to pay compensation to the insured, because the value of the claim insured submit exceeds the cliam estimated by the insurer. Those conditions can be anticipated if the insurance company has reserve funds. This study aims to find the right reserve value for insurance companies that have been adapted to endowment life insurance using the New Jersey and Full Preliminary Term method. Based on the data analysis carried out, it was concluded that the New Jersey reserve value for male from 1st year to 49th year is greater than that for females. Meanwhile, for Full Preliminary Term reserves, the value of male’s reserves from the 1st year to the 49th year is relatively always greater than that of females. This research could be used as a reference for insurance company to consider the better method in calculating premium reserves based on its policyholder profile
Forecasting The Number of Aircraft Passengers Arriving Through Soekarno-Hatta Airport Using Arima Model Windi Marnizal Putri; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3970

Abstract

Soekarno-Hatta International Airport is well known as the busiest airport in Indonesia with the number of airplane passengers normally grow from year to year. In 2010, there were more than 43 million passengers, and had increased up to 62.4 million over the year 2011. Risk of overcapacity became an issue. Thus, in the following year, the airport was planned for an expansion.  Predicting the frequency of passengers can be helpful for future planning and to improve airport facilities and policy. This research used Autoregressive Integrated Moving Average (ARIMA) to forecast the number of aircraft passengers. ARIMA (0,1,1) is the most suitable model used with MAPE 110%, the results is 2,405,205 passengers. Actual data and predictive data are not much different
Identifying Fraud in Automobile Insurance Using Naïve Bayes Classifier Dadang Amir Hamzah; Annisa Sentya Hermawan; Shintya Jasmine Pertiwi; Syarifah Intan Nabilah
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3971

Abstract

In this article, the Naïve Bayes Classifier is employed to detect fraud in automobile insurance. The Naïve Bayes classier is a simple probabilistic method based on the Bayes theorem. The data used in this article is determined from databricks.com which consists of 40 attributes and 1000 entries. The target attribute that will be predicted consists of two categories,” yes" or "no", which inform whether there is a fraud or not. The Data is split into training and testing with suitable proportions. Based on training data, the Naïve Bayes Classifier is applied to the testing data and returns the predictions data. Then, the prediction data is compared with the actual data to see the performance of the method. The result shows that the Naïve Bayes Classifier gives a good result to predict the insurance fraud with 78% accuracy, 67% precision, 3% of recall,  and  6% of F1 score  for “Yes”
Forecasting of YG Entertainment Stock Prices February 2022-August 2022 Using Arima Model Novia Galuh Ramadhanty; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3972

Abstract

The stock price in investing is the main factor in determining whether an investor will invest there. With stock price prediction research, investors have an idea of whether to invest in the company. YG Entertainment is a public company in the entertainment sector with many artists and entertainment projects that have fluctuating prices. With the ARIMA (Autoregressive Integrated Moving Average) forecasting method, we can predict YG Entertainment's stock price. In this article, YG Entertainment's prediction using the ARIMA model results in a MAPE error rate of 11% with the best model being ARIMA (0,1,0). The error of the model are 33160 x103 MSE, 5758.543 RMSE, and 4366.446 MAE. This forecast will produce good output as consideration for investor who interesting buy YG Entertainment stock price
Prediction of Loan Status Using Logistics Regression Model and Naïve Bayes Classifier Christabell Christabell; Edwin Setiawan Nugraha; Karunia Eka Lestari
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3968

Abstract

Conducting an evaluation process of prospective debtors is important for creditors to reduce the risk of default. For this reason, the research aims to construct a model that can determine whether a prospective applicant's credit application is recommended to be accepted or rejected by using the method of logistic regression and naïve Bayes classifier. We used a dataset of gender, married, dependent, education, self-employed, applicant income, co-applicant income, loan amount, loan amount term, credit history, and property area as predictor variables and loan status as a response variable. The results show that the performance measures, including accuracy, precision, recall, and F1 score of the logistics regression method, are 85.9%, 83.82%, 100%, and 91.2%, while the naïve Bayes classifier is 84.62%, 83.58%, 98.2%, and 90.32%. Since the performance measures of logistic regression are bigger than naïve Bayes classifier, it suggests that logistic regression is better than naïve Bayes classifier

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