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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Kubik Jurnal Matematika: MANTIK Teorema: Teori dan Riset Matematika International Journal of Supply Chain Management Eksakta : Berkala Ilmiah Bidang MIPA FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Kumawula: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika KUBIK: Jurnal Publikasi Ilmiah Matematika International Journal of Business, Economics, and Social Development Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Mathematical Sciences and Applications Journal
Betty Subartini
Departemen Matematika, FMIPA, Universitas Padjadjaran
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Journal : International Journal of Quantitative Research and Modeling

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Portfolio Analysis Using the Markowitz Model with Stock Lot Constraints and Target Returns or Without Target Returns Asri Rula Hanifah; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol 3, No 4 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i4.358

Abstract

Stock investment activities are inseparable from returns and risk, so an investor needs expertise to minimize investment risk. One way is by forming an optimal portfolio. The purpose of this research is to determine the number of stock lots in the optimal portfolio. This research analyzes the closing prices of stocks during the research period with the criteria of stocks being listed on the IDX30 index consecutively for 20 periods and belonging to the large cap group (the stock market capitalization exceeds $10 billion). Then the number of stock lots is calculated using the Markowitz model with stock lot constraints and target returns or without target returns. From the selected stocks, an optimal portfolio is formed using Microsoft Excel. Based on the research results, a combination of an optimal portfolio with a target return is ASII: 5, BBCA: 10, BBNI: 23, BBRI: 1, BMRI: 23, TLKM: 93, UNVR: 12, where the risk is 0,000149 and the target expected return is 0,00155. Meanwhile, the optimal portfolio without a target return is ASII: 8, BBCA: 7, BBNI: 32, BBRI: 40, BMRI: 9, TLKM: 62, UNVR: 17, where a risk is 0,000147 and the expected return is 0,00148. This research can be used as a consideration for investors in determining investment portfolios.
Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method Pamungkas, Abram Chandra Aji; Subartini, Betty; Susanti, Dwi
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v%vi%i.594

Abstract

Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.
Actuarial Pension Fund Using the Projected Unit Credit (PUC) Method: Case Study at PT Taspen Cirebon Branch Office Amalia, Hana Safrina; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.745

Abstract

The pension fund program is a program held by the government to ensure the welfare of Civil Servants (PNS) in retirement as old-age security. The pension program for civil servants is managed by a pension fund, PT Taspen (Persero). Actuarial calculations of pension funds need to be carried out to determine the amount of normal contributions and actuarial liabilities that must be paid by pension plan participants and companies. The actuarial calculation of pension funds used by PT Taspen in managing civil servant pension funds is the Accrued Benefit Cost which determines in advance the benefits that will be obtained by participants. The Projected Unit Credit (PUC) method is one part of the Accrued Benefit Cost. This study aims to determine normal contributions and actuarial liabilities using the Projected Unit Credit (PUC) method for civil servant pension program participants of PT Taspen (Persero) Cirebon Branch Office. The calculation results show that the PUC method provides a more accurate calculation of the estimated normal contributions and actuarial liabilities of the company. This study is expected to be a reference for other companies in managing employee pension funds using an actuarial approach.
Comparison of Stock Price Forecasting with ARIMA and Backpropagation Neural Network (Case Study: Telkom Indonesia) Carissa, Katherine Liora; Subartini, Betty; Sukono, Sukono
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.v6i1.896

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.
Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Nurdyah, Himda Anataya; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1017

Abstract

The importance of the food sector to Indonesia's economy makes it one of the most attractive sectors to consider in an investment portfolio. An optimal portfolio is the best choice for investors among various efficient portfolios, aiming to maximize returns while minimizing risk. Moreover, since investment is inherently associated with fluctuating stock prices, accurate forecasting is necessary to anticipate future stock movements. This study aims to accurately predict stock prices and construct an optimal portfolio consisting of five food sector stocks listed on the Indonesia Stock Exchange, namely DMND, ICBP, HOKI, INDF, and ULTJ. Stock price predictions are generated using the Holt-Winter method, which can identify seasonal patterns and trends from historical data. The predicted stock prices are then used to calculate returns, which serve as the basis for portfolio optimization using the Mean-Variance model. The results show that the Holt-Winter method successfully produces accurate stock price forecasts, with Mean Absolute Percentage Error (MAPE) values for all stocks below 10%. These forecasts are used to calculate returns in the portfolio optimization process. The optimal portfolio composition is determined with the following weight proportions: HOKI (4%), ICBP (18%), ULTJ (21%), DMND (26%), and INDF (30%). This portfolio yields an expected return of 0.0441% and a portfolio variance of 0.0063%, reflecting a balanced trade-off between potential return and risk.
IDX30 Stock Portfolio Optimization Using Genetic Algorithm Based on Capital Asset Pricing Model Rahmadhisa, Nayra Pavita; Susanti, Dwi; Subartini, Betty
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.981

Abstract

The stock market plays a vital role in supporting economic growth by serving as a primary channel for companies to raise capital and for investors to gain profits through long-term investments. In practice, one of the biggest challenges for investors is identifying which stocks are worth purchasing and how to allocate their funds optimally. One commonly used approach to evaluate stock feasibility is the Capital Asset Pricing Model (CAPM), which helps identify undervalued and overvalued stocks based on the relationship between systematic risk and expected return. Additionally, it is necessary to determine the optimal investment weight allocation. Therefore, this study combines the CAPM method for stock selection and Genetic Algorithm, a metaheuristic approach capable of finding optimal solutions in complex problems, to determine the optimal portfolio weight composition. The object of this study includes stocks listed in the IDX30 index during the period from February 2021 to November 2023. The results show that five stocks—ADRO, BBCA, BBNI, KLBF, and TLKM—are classified as undervalued according to the CAPM method and are recommended for inclusion in the optimal portfolio. Portfolio optimization using the Genetic Algorithm results in the following stock weight composition: ADRO 26.55%, BBCA 36.20%, BBNI 9.09%, KLBF 12.20%, and TLKM 15.96%, with a Sharpe Ratio of 4.043906. The expected return and risk of the optimal portfolio are 0.00067373 and 0.00012407, respectively.
Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sianturi, Sri Novi Elizabeth; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
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.
Analysis of Pet Owners' Willingness to Pay for Pet Insurance Premiums in DKI Jakarta Using Logistic Regression Model Adib, Andhita Zahira; Riaman, Riaman; Subartini, Betty
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.578

Abstract

Pets provide many benefits to their owners, both physically and mentally. Pet lovers are increasingly aware of the importance of proper health and care for their beloved animals. This has led pet enthusiasts to consider pet insurance. In participating in insurance, there are factors that influence the willingness of pet owners to pay premiums. The objective of this research is to determine the premium for pet insurance and analyze the factors influencing the Willingness To Pay (WTP) of pet owners. This study utilizes choice modeling format by conducting surveys to identify the factors influencing the purchase of pet insurance. Subsequently, binary logistic regression model analysis using the Maximum Likelihood Estimation (MLE) method and the Newton-Raphson Iteration approach is employed to analyze the factors influencing the magnitude of WTP. The research results show that the average willingness to pay for pet insurance premiums is IDR128,574.76 per year. Factors influencing the decision of pet owners include the number of family dependents and awareness of the importance of participating in pet insurance. The likelihood of cat owners being willing to pay pet insurance premiums is 0.8691 or 86.91%.
Calculation of Term Life Insurance Premium Reserves with Fackler Method and Canadian Method Zakirah, Khalilah Razanah; Subartini, Betty; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
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.
Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks Wijaya, Silvia; Subartini, Betty; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.591

Abstract

Investment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%.
Co-Authors - Riaman Adib, Andhita Zahira Agus Santoso Agus Supriatna Agus Supriatna Aisyah Nurul Aini Alfan Azkiya Robbani Amalia, Hana Safrina Annisa Aprillia Annisa Nur Falah Annisa Nur Falah, Annisa Nur Apipah Jahira, Juwita Ary Robayani Asep K Supriatna Asep K Supriatna, Asep K Asri Rula Hanifah Azimah Lisan Robbani Banowati, Puspa Dwi Ayu Budi N. Ruchajana Budi N. Ruchajana, Budi N. Carissa, Katherine Liora Diah Chaerani Dian Nuryani Dini Aulia Dwi Susanti Eddy Djauhari Edi Kurniadi Edi Kurniadi Edi Kurniadi Edi Kurniadi Edi Kurniadi Elis Hertini Endang Rusyaman Endang Soeryana Hasbullah Felfin Ulfah Annisa Firdaniza Firdaniza Firdaus, Muhammad Rayhan Ghazali, Puspa Liza Herlina Napitupulu Jeane R. M. D. P Chantique Jumadil Saputra Kankan Parmikanti Muhammad Iqbal Al-Banna Ismail Nahda Nabiilah Novitasari, Ela Nurdyah, Himda Anataya Nursanti Anggriani Nurul Fadilah Nurul Gusriani, Nurul Olivia Anjanette Pamungkas, Abram Chandra Aji Putri Adhira Novalia Putri Chaerunnisa Febryanti Putri, Sherina Anugerah R Wulantini R Wulantini, R Rahmadhisa, Nayra Pavita Rahman, Rezki Aulia Reisnanda, Aldino Riaman . Riaman Riaman Riaman Riaman Riaman Riaman Riaman Riaman, Riaman Risdawati Risdawati Sampath, Sivaperumal Saputra, Jumadil Sianturi, Sri Novi Elizabeth Silvia Wijaya Sisilia Sylviani Sonia . Sukono Sukono Tampubolon, Carlos Naek Tua Tegar Ridwansyah Yeremia Herry Parulian Yeremia Herry Parulian, Yeremia Herry Zahra, Ami Emilia Putri Zakirah, Khalilah Razanah