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All Journal International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Mathematics, Statistics, and Computing
Bisyarah, Sania
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics Physics

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Profit Planning Analysis with Break Even Point Approach at PT. Sinar Jaya Bisyarah, Sania; Elizabeth S, Sri Novi
International Journal of Global Operations Research Vol. 5 No. 3 (2024): International Journal of Global Operations Research (IJGOR), August 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i3.327

Abstract

Sales management is important for a company to control profitability and avoid the risk of loss.  In reality, companies are often faced with the possibility of loss and the ability to manage sales can affect the magnitude of this risk. Break Even Point (BEP) analysis can be an effective strategy to determine the minimum sales amount so that the company avoids losses. In this study, analyzed sales at PT Sinar Jaya using BEP analysis for the period January to December 2023. The results of the analysis show that the company is consistently able to achieve BEP and earn profits above 7% every month which reflects operational efficiency in profit stability. In addition, the Margin of Safety (MoS) value which is always above 20%, with a peak reaching 70%, shows that the company has strong protection against risk.
Bankruptcy Prediction Analysis of General Insurance Companies Using the Ohlson Model Maharani, Asthie Zaskia; Bisyarah, Sania
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.336

Abstract

General insurance companies play an important role in maintaining economic stability by transferring financial risks from individuals and companies to insurance companies. However, insurance companies are not immune to the risk of bankruptcy that can arise due to factors such as inability to manage claims, premium fluctuations, and insufficient capital. Early detection of potential bankruptcy becomes very important to prevent greater losses. This study aims to analyze the prediction of bankruptcy in general insurance companies in Indonesia using the Ohlson Model. The Ohlson model is based on logistic regression, taking into account several financial variables such as leverage, profitability, and company size to estimate the probability of bankruptcy. The results of the study are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets. From the analysis, it was found that out of 13 general insurance companies listed on the Indonesia Stock Exchange (IDX), the Ohlson value for all companies is below 0.38, which indicates that the sampled companies still have fairly good financial stability. The research results are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets.
Investment Portfolio Optimization on Technology Sector Stocks Using Mean-Variance Model with Asset-Liability Based on ARIMA-GARCH Approach Bisyarah, Sania
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.864

Abstract

In this era of rapid technological advancement, various sectors are experiencing changes, one of which is investment. Investors are starting to turn their attention to technology sector stocks as new investment targets. However, investments are inherently linked to return and risk levels and stock prices can be highly volatile. Therefore, forming an optimal investment portfolio is very important to achieve a balance between return and risk. In addition, coping with volatile stocks is also very important. The ARIMA-GARCH time series model is a method that can be used to deal with such volatility. A popular strategy for portfolio optimization is to use the Mean-Variance model, also known as the Markowitz model. This study aims to form an optimal portfolio consisting of five technology sector stocks in Indonesia with the codes AXIO, DIVA, EDGE, MCAS, and CASH using the Mean-Variance model with assets-liabilities equipped with the ARIMA-GARCH approach. Based on the results of the study, the optimal portfolio is obtained with the composition of each weight is 23.16% of the capital allocated to AXIO; 2.95% for DIVA; 56.48% for EDGE; 6.36% for MCAS; and 11.05% for CASH. The weight allocation composition can generate a portfolio return of 0.0066 and a variance (risk) return of 0.0082.
Calculation of Pension Funds for TNI Group IIIA Using the Individual Level Premium Method Bisyarah, Sania; Novi Elizabeth S., Sri
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 3 (2024): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v2i3.117

Abstract

Prosperity in retirement is one of the important aspects to achieve in the career path of every individual, including the Indonesian National Army (TNI) which relies on pension funds as a source of post-retirement income. Pension funds manage programs that promise financial benefits to participants after they retire. In this context, the study aims to present the calculation of pension funds for TNI class IIIA using the Individual Level Premium method. This method allocates the total pension benefit equally each year. As a result, it is found that the Individual Level Premium method provides a greater pension benefit value than the benefit value using the proportion of salary from ASABRI. This shows that this method is effective in calculating retirement benefits.
Investment Portfolio Optimization on Technology Sector Stocks Using Mean-Variance Model with Asset-Liability Based on ARIMA-GARCH Approach Bisyarah, Sania
International Journal of Quantitative Research and Modeling Vol. 6 No. 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.864

Abstract

In this era of rapid technological advancement, various sectors are experiencing changes, one of which is investment. Investors are starting to turn their attention to technology sector stocks as new investment targets. However, investments are inherently linked to return and risk levels and stock prices can be highly volatile. Therefore, forming an optimal investment portfolio is very important to achieve a balance between return and risk. In addition, coping with volatile stocks is also very important. The ARIMA-GARCH time series model is a method that can be used to deal with such volatility. A popular strategy for portfolio optimization is to use the Mean-Variance model, also known as the Markowitz model. This study aims to form an optimal portfolio consisting of five technology sector stocks in Indonesia with the codes AXIO, DIVA, EDGE, MCAS, and CASH using the Mean-Variance model with assets-liabilities equipped with the ARIMA-GARCH approach. Based on the results of the study, the optimal portfolio is obtained with the composition of each weight is 23.16% of the capital allocated to AXIO; 2.95% for DIVA; 56.48% for EDGE; 6.36% for MCAS; and 11.05% for CASH. The weight allocation composition can generate a portfolio return of 0.0066 and a variance (risk) return of 0.0082.
Co-Authors Elizabeth S, Sri Novi Maharani, Asthie Zaskia Novi Elizabeth S., Sri