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All Journal International Journal of Quantitative Research and Modeling
Betty Subartini
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Determination of Insurance Premiums to Mitigate the Risk of Company Losses Due to Supplier Failure Using Black-Scholes-Merton Model Jessica Novia Sitepu; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i4.447

Abstract

The Micro and Small Enterprises (MSMEs) sector in Indonesia has made a significant contribution to the Indonesian economy. However, MSMEs in Indonesia face various challenges that may occur in the future, for example, supplier failure. Therefore, it is essential to determine the right form of risk mitigation to reduce the impact of supplier failure for MSMEs, and one such approach is to have insurance. This study aims to calculate the premium price using the Black-Scholes-Merton model approach. The data used is the aggregate losses experienced by MSMEs fostered partners of PT Wijaya Karya (Persero) Tbk. Data simulation was generated on lognormal distribution to determine the premium price. The application of the Black-Scholes-Merton model on the calculations showed that MSMEs have to pay a premium of IDR 4.165.061 for one year.
Calculation of Term Life Insurance Premium Reserves with Fackler Method and Canadian Method Khalilah Razanah Zakirah; Betty Subartini; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.589

Abstract

Every individual around the world goes through the life cycle of birth and continues their journey with unique experiences. The uncertainty of the future, which includes both happiness and calamity, is a universal aspect of human life. Life risks, such as illness and death, are an unavoidable reality for every individual in this world. Life insurance is one of the solutions to manage these risks, with term life insurance being one of the options. The focus of this research lies on term life insurance, with the aim of calculating premium reserves using the Fackler and Canadian methods. This research is concerned with the process of calculating premium reserves, and the results show that the Fackler method produces a larger premium reserve value compared to the Canadian method. Recommendations are given to companies to use the Fackler Method in calculating term life insurance premium reserves to avoid potential losses that could occur if using the Canadian method. The choice of premium calculation method is a strategic key in effective risk management for the company.
Comparison of Stock Price Forecasting with ARIMA and Backpropagation Neural Network (Case Study: Telkom Indonesia) Katherine Liora Carissa; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 1 (2025): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.896

Abstract

The growth of capital market investors in Indonesia is increasing every year. The most popular investment instrument is stocks. One of the stocks on the Indonesia Stock Exchange (IDX) is the Telkom Indonesia (TLKM). Through stock investment, investors can make a profit by utilizing stock prices in the market. However, stock price fluctuations are uncertain. Therefore, modeling is needed to be able to predict stock prices more accurately. The purpose of this study was to find an appropriate time series model and Neural Network model architecture, and to measure the accuracy of the two models in predicting future stock prices of TLKM. The study was conducted using the Autoregressive Integrated Moving Average (ARIMA) model and Backpropagation Neural Network (BPNN). For comparison, the Mean Absolute Percentage Error (MAPE) method was used. The data used in both models were the stock prices of Telkom Indonesia (TLKM) from September 1, 2023 to September 30, 2024. The result shows that the best ARIMA model, selected based on the least Akaike Information Criterion (AIC) value, is ARIMA(0,1,3) with a MAPE value of 1.20%. Meanwhile, the best BPNN model selected from the smallest testing Mean Squared Error (MSE) value, is BPNN(1,3,1) with a MAPE value of 1.17%. Among those two models, the BPNN model is more accurate because it has less MAPE value compared to the ARIMA one. The results of this research can be considered in forecasting TLKM stock price in the future.
Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sri Novi Elizabeth Sianturi; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1001

Abstract

Stock mutual funds gained popularity among the public as an investment alternative due to the convenience they offer, especially for beginner investors who have limited time and investment knowledge. Compared to money market and bond mutual funds, these mutual funds offer higher potential returns but also come with higher risks due to value fluctuations, so forecasting stock mutual fund prices is essential to minimize losses. Since stock mutual fund prices is time series data, this research employs two forecasting models such as Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR). The objective of this research is to determine the best-performing model between ARIMA and NNAR, and compare their forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The data used consists of daily closing prices of stock mutual funds from March 1, 2022, to March 31, 2025, with the criteria that the selected issuers have been operating for more than five years. The results of this research show that the best ARIMA and NNAR for the RNCN are ARIMA([1],1,0) and NNAR(2,2); for TRAM are ARIMA(0,1,[1]) and NNAR(4,1); for SCHRP are ARIMA(0,1,[1]) and NNAR(4,2); for MICB are ARIMA([1],1,0) and NNAR(2,2); and for BNPP are ARIMA([1],1,0) and NNAR(5,1). The MAPE values in the same order are 6.83% and 5.49%; 6.53% and 5.75%; 8.57% and 7.10%; 8.39% and 8.75%; 8.51% and 7.30%. Based on the comparison, NNAR outperformed ARIMA in four out of five mutual funds, with lower MAPE values and also marked by the ARIMA model tend to produce stable or unchanging values over the long term. The results of this research are expected to assist investors in consederating by choosing NNAR model, both in the short and long term, to obtain better stock mutual fund price forecasts.
Co-Authors Jessica Novia Sitepu Katherine Liora Carissa Khalilah Razanah Zakirah Riaman Riaman Sri Novi Elizabeth Sianturi Sukono Sukono