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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jurnal Cendekia : Jurnal Pendidikan Matematika International Journal of Supply Chain Management FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika International Journal of Business, Economics, and Social Development International Journal of Research in Community Services Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research Euclid Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)

Riaman Riaman

Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran

Author-ID : 605226

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Estimation of the Extreme Distribution Model of Economic Losses Due to Outbreaks Using the POT Method with Newton Raphson Iteration Riza Adrian Ibrahim; Sukono Sukono; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol 2, No 1 (2021)
Publisher : Research Collaboration Community (RCC)

Extreme distribution is the distribution of a random variable that focuses on determining the probability of small values in the tail areaof the distribution. This distribution is widely used in various fields, one of which is reinsurance. An outbreak catastrophe is non-natural disaster that can pose an extreme risk of economic loss to a country that is exposed to it. To anticipate this risk, the government of a country can insure it to a reinsurance company which is then linkedto bonds in the capital market so that new securities are issued, namely outbreakcatastrophe bonds. In pricing, knowledge of the extreme distribution of economic losses due to outbreak catastrophe is indispensable. Therefore, this study aims to determine the extreme distribution model of economic losses due to outbreak catastrophe whose models will be determined by the approaches and methods of Extreme Value Theory and Peaks Over Threshold, respectively. The threshold value parameter of the model will be estimated by Kurtosis Method, while the other parameters will be estimated with Maximum Likelihood Estimation Method based on Newton-Raphson Iteration. The result of the research obtained is the resulting model of extreme value distribution of economic losses due to outbreak catastrophe that can be used by reinsurance companies as a tool in determining the value of risk in the outbreak catastrophe bonds.

Determining the Price of Fisherman Micro Insurance Premiums Using the Aggregate Risk Model Approach in Cirebon Regency Ratih Kusumadewi; Riaman Riaman; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol 3, No 3 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i3.346

Catastrophe such as hurricanes, heavy rains, and similar occurrence pose serious threats and risks to fishermen's livelihoods as well as losses from damage to their assets. Therefore, it is necessary to have special insurance to protect the fishermen's assets from financial losses due to the risks that can occur, namely Fisherman Micro Insurance. Micro-insurance is an insurance product that is intended for low-income people with features and administration that are simple, easy to obtain, economical prices and immediately in the completion of the provision of compensation. Fisherman's micro insurance guarantees assets in the form of fishing equipment in the occurrence of a risk of an accident causing damage, this insurance product protects against worries without a large premium burden. This study aims to calculate the premium price with an aggregate risk model approach. The data used is data on fisherman’s losses if they did not go to sea which obtained by surveys. The occurrence data follows the Poisson distribution, and the loss data follows the Exponential distribution. Parameter Estimation was carried out using the Maximum Likelihood Estimation. The estimation results from numbers of occurrence and the amount of losses are used to estimate the collective risk model. Estimators of the average and variance of the aggregate risk are used to determine the premium. The results of the premium selection in this study amounted to IDR 153.861.958.00. The premium amount is a collective premium which is the result of a calculation based on the standard deviation principle.

Application of Single Index Model to Determine Optimal Stock Portfolio (A Case Study on IDX30 in 2022) Emmanuel Parulian Sirait; Kankan Parmikanti; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol 4, No 3 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i3.493

Stock represent proof of ownership or participation of an individual or entity in a company. Investors gain profits from shares through capital gains and dividends. The difficulty in selecting an optimal composition of a stock portfolio is a major concern for investors. This study aims to determine the optimal composition of a stock portfolio, calculate the expected returns in the future, and assess the potential risks that investors may encounter later on. The data for this research consists of stocks listed on the IDX30 Index throughout the year 2022, which consistently appear in every six-month evaluation. The analysis is conducted using a single-index model. Based on the findings of this study, the following ten stocks are identified as the optimal portfolio constituents: KLBF with a weight of 17.20%, BBRI with a weight of 17.18%, BBCA with a weight of 17.08%, PTBA with a weight of 12.46%, BBNI with a weight of 9.89%, UNVR with a weight of 8.33%, INKP with a weight of 8.66%, ICBP with a weight of 5.56%, BMRI with a weight of 3.25%, and UNTR with a weight of 0,39%. The expected return from the formed portfolio is 0,1% per day, with a corresponding risk of 0,004%.

Optimum Fund Allocation Strategy by Considering the Company's Assets and Liabilities Qurrotu Aini; Dwi Susanti; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol 4, No 3 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i3.444

Investment is essentially placing some funds at present with the expectation of future profits. The basic thing that an investor needs to know is that there is a risk that follows the profit/return. In determining the proper allocation of funds, an investor needs to consider the company's assets and liabilities. Company assets can be in the form of shares, property, and others. Meanwhile, the company's liabilities include debts and other obligations. One of the sectors whose company value has stagnated or increased during the Covid-19 Pandemic is the financial sector. Securities companies are a sub-sector of the financial sector which has a fairly strong position during the Pandemic. This research aims to determine the weight of fund allocation in each company forming the optimum portfolio and to see the effect of the company's assets and liabilities on the formation of the optimum portfolio. One of the methods used is the Lagrange Multiplier method for model formulation. The results of this study show that the optimal portfolio weight of PANS companies is 16.31% with an allocation of funds amounting to Rp163.612.976,00, the optimum portfolio weight of RELI companies is 83.003% with an allocation of funds of Rp830.029.681,00, and the optimum portfolio weight of TRIM companies is 0.636% with the allocation of funds amounting to Rp6.358.243,00. In this study, it was also found that the greater the percentage difference between the company's assets and liabilities, the greater the company's optimum portfolio weight.

Mean-Variance Optimal Portfolio Selection with Risk Aversion on Transportation and Logistics Sector Stocks Based on Multi-Criteria Decision-Making Putri, Aulya; Riaman, Riaman; 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.v6i1.895

The importance of the transportation and logistics sector to a country's economy, coupled with the growth of this sector in Indonesia, requires investment support for this sector to continue to grow. Therefore, stocks in the transportation and logistics sector are attractive for investment portfolio consideration. The optimal portfolio selection is to minimize the risk with the expected return. In the formation of an investment portfolio, the problem is how to determine the weight of capital allocation in order to get the maximum return while still considering the risk in each stock, by considering several criteria in decision making. This study was conducted to determine the best stock selection in the transportation and logistics sector listed on the Indonesia Stock Exchange, and determine the optimal weight in the investment portfolio. The method used is Multi-Criteria Decision Making (MCDM), namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using 15 financial metrics as relevant criteria in stock selection. Furthermore, to determine the allocation weight to form an optimal stock portfolio using the Mean-Variance model with Risk Aversion. The stocks analyzed were 28 stocks in the transportation and logistics sector. The results of research based on MCDM selected 9 stocks, namely MITI, BIRD, HATM, TMAS, JAYA, PPGL, BPTR, ASSA, and RCCC. However, TMAS, PPGL, and BPTR stocks are not included in portfolio formation because they have a negative average return. Based on the optimization results, the allocation weights of the 6 stocks included in the optimal portfolio are BIRD (37.7%), JAYA (24.6%), MITI (12.9%), HATM (9.9%), ASSA (7.5%), and RCCC (7.4%). The results of this study are expected to be a consideration in making investment decisions.

Investment Portfolio Optimization Using Ant Colony Optimization (ACO) Based on Fama-French Three Factor Model on IDX High Dividend 20 Stocks Maharani, Asthie Zaskia; Susanti, Dwi; Riaman, Riaman
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.v6i2.978

Stock investment is one of the investment options that provides both profit and risk for investors. In an effort to maximize profits and minimize risks, investors need an optimal portfolio. The optimal portfolio is a portfolio selected from a collection of efficient portfolios. To form an optimal portfolio, this study combines the Fama-French Three Factor Model (FF3FM) for stock selection and Ant Colony Optimization (ACO) for stock weight optimization in the portfolio. FF3FM considers more factors resulting in more comprehensive stock selection than other methods. While ACO has the ability to explore the solution space widely and efficiently, minimizing the risk of getting stuck on a local solution. The performance of the optimal portfolio is measured using the Sharpe Ratio which considers total risk, thus providing an overview of overall investment efficiency. The research object used is quarterly stock data on IDX High Dividend 20 from the Indonesia Stock Exchange (IDX) for the period 2020-2023. Of the 20 stocks, 12 stocks were selected that were consistently included in the index during the 2020-2023 period. By selecting stocks using the FF3FM method, 10 efficient stocks were selected, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, ITMG, PTBA, TLKM, and UNTR. Portfolio optimization using ACO produces a portfolio return of 0.0473 and a risk of 0.0257 with the weight of each ADRO stock of 6.90%, BBCA of 17.24%, BBNI of 10.34%, BBRI of 27.59%, INDF of 3.45%, ITMG of 27.59%, TLKM of 3.45%, and UNTR of 3.45%. The results showed that the integration of FF3FM and ACO was able to form a portfolio with optimal performance with a Sharpe Ratio value of 1.41868, which means that the portfolio return is greater than the portfolio risk.

Comparative Analysis of Normal Pension Benefits Using the Attained Age Normal Method and the Individual Level Premium Method Hukama, Atha; Parmikanti, Kankan; Riaman, Riaman
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.v6i2.946

Pension programs are among the most important forms of employee compensation, offering financial security after retirement. This study aims to calculate the company’s initial payroll contributions to determine regular contributions, actuarial liabilities, and pension benefits using two actuarial projection methods: the Attained Age Normal (AAN) and Individual Level Premium (ILP) methods. The analysis is based on employee data from Puskesmas Binjai Estate, including age, salary, and years of service. It includes computations of pension benefits, normal costs, actuarial liabilities, and net benefits received by employees under each method. The results reveal that the length of service significantly affects both the value of contributions and the actuarial liabilities. Employees with longer service periods result in higher contribution requirements and greater liabilities. Moreover, the Attained Age Normal method produces higher pension benefits compared to the Individual Level Premium method for long-serving employees. However, both methods present financial challenges for employers, as they require higher contributions relative to the benefits promised. Consequently, companies must allocate substantial funding to meet their pension obligations. This study provides a comparative perspective that can assist decision-makers in selecting an actuarial method that balances benefit adequacy and financial sustainability.

Portofolio Optimization of Mean-Variance Model Using Tabu Search Algorithm with Cardinality Constraints Ma’mur, Lutfi Praditia; Riaman, Riaman; 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.v6i2.1010

Stock investment is increasingly attractive to Indonesians, especially through the IDX30 index, which is known to have high liquidity and solid company fundamentals. In forming an optimal stock portfolio, investors are faced with the challenge of maximizing return and minimizing risk simultaneously. An optimal portfolio is defined as a combination of assets that provides the highest expected return at a certain level of risk, or the lowest risk for the expected level of return. This study aims to form an optimal portfolio on the IDX30 index by considering cardinality constraints, which limit the maximum number of stocks in the portfolio. From 30 IDX30 stocks, 20 stocks were selected based on consistency of existence during the period February 1, 2023 to January 31, 2025. Next, 8 stocks that have positive expected return values are selected, and from these 8, 4 efficient stocks are selected using cardinality constraints. Selection is done with the Tabu Search algorithm, a memory-based metaheuristic optimization method used to find the best solution by avoiding previously explored solutions. The portfolio is formed using the Mean-Variance model, resulting in an allocation of BMRI (30,02%), PTBA (35,18%), INDF (2,48%), and BRPT (32,32%), with an expected return of 0,00207 and a variance of 0,001587.

Analysis of the French Five Factors Fama Model on Excess Return of Stocks Listed on IDXBUMN20 for the Period 2020-2023 Putri, Linda Damayanti; Riaman, Riaman; 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.v6i2.966

Excess return is the difference between the rate of return earned on an investment and the rate of risk-free return in a given period. This shows how much return is received because they are willing to take risks in investing. This study aims to analyze the Fama French Five Factor model on the excess return of stocks listed in IDXBUMN20 2020-2023 period. The factors in the model are market factors, size factors, book to market ratio, profitability, and investment. The population in this study amounted to 20 companies registered in the IDXBUMN20 index, the sample selection in this study used the purposive sampling method and a sample of 12 companies was obtained. The data used in the study are close price, number of shares outstanding, Bank Indonesia (BI) interest rate, and company financial statements. The analysis method used was the Common Effect Model (CEM) panel data regression analysis. Based on hypothesis testing, market factors were obtained which only had an effect on excess returns. This factor shows the influence of the ups and downs of market performance on the price of a stock.

Implementation of Simulated Annealing Algorithm for Portfolio Optimization in Jakarta Islamic Index (JII) Stocks with Mean-VaR Riadi, Nadia Putri; Riaman, Riaman; 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.v6i2.1016

One of the challenges for investors in the investment world is to manage the stock portfolio optimally. The main objective of portfolio optimization is to obtain maximum profit with a controlled level of risk. This study aims to find a portfolio combination that provides the best return with a more controllable risk than the conventional method, using Simulated Annealing. This research method applies the Mean-Value at Risk (Mean-VaR) approach in measuring portfolio performance and uses the application of the Simulated Annealing algorithm as an optimization method to determine the optimal investment weight on stocks in the Jakarta Islamic Index (JII), so as to obtain a portfolio with the best performance compared to a simple weighting strategy. The data used in this study is the daily closing price of stocks listed in the JII during the period January 3, 2022 - January 2, 2024. Based on the results and discussion, there are 7 stocks included in the formation of the optimal portfolio of JII index stocks, namely ADRO, ICBP, INKP, ITMG, MIKA, TPIA, and UNTR. The weight allocation of each stock generated by the Simulated Annealing method for the period is for ADRO shares 7,4177%; ICBP 1,7817%; INKP 7,3369%; ITMG 15,0006%; MIKA 2,5894%; TPIA 63,5506%; and UNTR 2,323%. The optimal portfolio of the Mean-VaR model with the Simulated Annealing method is generated when the risk tolerance is 0 (τ=0), with a return or return of 0,001923 and a VaR risk level of 0,029788. This approach is expected to be an alternative for investors in determining investment strategies based on Islamic stocks in Indonesia.

Co-Authors AGUS SUPRIATNA Aldino Reisnanda Alit Kartiwa Anang Muhajirin Andhita Zahira Adib Annisa Aprillia Ariyanti, Devi Arla Aglia Yasmin Ary Robayani Asthie Zaskia Maharani Atha Hukama Aulianda Anisa Putri S. R. Aulya Putri Ayyinah Nur Bayyinah Azizah Rini Widyani Bayyinah, Ayyinah Nur Betty Subartini Betty Subartini Betty Subartini Betty Subartini Dewi Ratnasari Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Edi Kurniadi Emmanuel Parulian Sirait Estu Putri Dianti Ghazali, Puspa Liza Hasbullah, Soeryana Herlina Napitupulu Hidayana, Rizki Apriva Hukama, Atha Iin Irianingsih Jumadil Saputra Kahar, Ramadhina Hardiva kalfin Kalfin Kankan Parmikanti Khalilah Razanah Zakirah Komar Komar Laksito, Grida Saktian Linda Damayanti Putri Luki Setiawan Luki Setiawan Lutfi Praditia Ma’mur Maharani, Asthie Zaskia Ma’mur, Lutfi Praditia MIFTAAHUL JANNAH Moisino, Misel Lindi Nahda Nabiilah Noriszura Ismail Novianti, Saqila Pramudhita, Annisa Pryimak, Evgen Putri Adhira Novalia Putri Chaerunnisa Febryanti Putri, Aulya Putri, Linda Damayanti Qurrotu Aini Radya Pratiwi Serila Raharjanti, Amalia RAHMAWATI, SEPTI Ramdhania, Tya Shafa Ratih Kusumadewi Riadi, Nadia Putri Riza Adrian Ibrahim Saefullah, Rifki Silvia Wijaya Soeryana Hasbullah Subartiny, Betty Sudartianto Sudartianto Sukono Sukono Supian, Sudradjat Susanto, Sunarta Syaâ€™imaa.HS, Audrey Ariij Tika Fauzia Tyrenia Rahmawati Ulfatmi, Ristifani Vimelia, Willen Wahid, Alim Jaizul Waway Tiswaya Widyani, Azizah Rini Willen Vimelia Yasir Salih Yasmin, Arla Aglia Yeremia Herry Parulian Yeremia Herry Parulian, Yeremia Herry Yudhi Andriyana Yulianus Brahmantyo Zahra, Ami Emelia Putri

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