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Operations Research: International Conference Series
Published by Indonesian Operations Research Association
ISSN : 27231739     EISSN : 27220974     DOI : https://doi.org/10.47194/orics
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics
Operations Research: International Conference Series (ORICS) is published 4 times a year and is the flagship journal of the Indonesian Operational Research Association (IORA). It is the aim of ORICS to present papers which cover the theory, practice, history or methodology of OR. However, since OR is primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of OR to real problems are especially welcome. In real applications of OR: forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community OR, education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation.
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021" : 5 Documents clear
Determination of VaR on BBRI Stocks and BMRI Stocks Using the ARIMA-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.178

Abstract

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBRI and BMRI stock using the ARIMA-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA model used for the mean model and the GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBRI and BMRI stock. The results obtained are the ARIMA(3,0,3)-GARCH(1,1) and ARIMA(2,0,2)-GARCH(1,1) model so with a significance level of 5% obtained Value-at-Risk of 0.04058 to BBRI stock and 0.10167 to BMRI stock.
Analysis of the Effect of Temperature and Rainfall on Coffee Productivity in Indonesia using the Cobb-Douglas model for Determining Insurance Premiums Novianti, Saqila; Riaman, Riaman; Sukono, Sukono
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.179

Abstract

Coffee is one of Indonesia's foreign exchange earners and plays an important role in the development of the plantation industry. Indonesia is a coffee bean producing country ranked 4th in the world after Brazil, Vietnam, and Colombia. The agricultural sector in Indonesia has risks and uncertainties including a decrease in production yields which results result in a decrease in farmers income. The risk of loss in coffee is caused by temperature and rainfall. Efforts that can be made to reduce losses are through risk transfer through agricultural insurance. The purpose of this study to analyze the effect of temperature and rainfall on coffee productivity in Indonesia and determine the insurance premium. This research uses data on coffee productivity, temperature, and rainfall from 1980-2019. The relationship between coffee productivity as a dependent variable while temperature and rainfall as an independent variable was used the Cobb-Douglas method. The results that will be obtained from this study indicate the temperature and rainfall affect coffee productivity in Indonesia, and obtain insurance issued by the farmers to the insurance companies. The results obtained from the data analysis show that temperature and rainfall have an effect on coffee productivity in Indonesia. The results of productivity predictions are used as the basis for determining the price of insurance premiums issued bye insurance companies.
Analysis of Microinsurance Demands Combined with Microcredit on Rice Farming by Using Utility Function Apipah Jahira, Juwita; Subartini, Betty; Sukono, Sukono
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.175

Abstract

Agriculture is a business that is prone to risk and uncertainty so farmers can face serious difficulties at any time. Especially for farmers in developing countries who are generally small farmers. To anticipate these risks and uncertainties, farmers can take agricultural insurance or apply for credit. Even though an agricultural insurance program is available, farmers are constrained by the limited amount of collateral and liquidity constraints. This study aims to analyze the demand for microinsurance combined with microcredit in rice farming. The analysis is carried out with utility functions and utility comparisons using ordinal comparison. Meanwhile, to determine optimal demand by maximizing the utility using an ordinal approach through analysis of budget line and indifference curve. The results show that the demand for insurance and the profitability of agricultural credit increases along with the lower demand for collateral when applying for agricultural loans. In addition, microinsurance combined with microcredit is more profitable for farmers when collateral is not requested when applying for agricultural credit. Based on the results of the case study, the optimal demand is obtained when the premium for Rice Farming Business Insurance (AUTP) is and the installments of BNI People’s Business Credit (BNI KUR) is
Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks Hidayana, Rizki Apriva; Napitupulu, Herlina; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.176

Abstract

Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.
Total Actuarial Liabilities and Normal Costs Using The Unit Credit Method Gusliana, Shindi Adha
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.177

Abstract

The pension fund program requires an actuarial calculation, such as the amount of actuarial obligations and normal costs for each pension fund participant. Total actuarial liabilities are calculated to show the company's liability for pension benefits for pension fund participants. Funding in pension funds is obtained from the normal costs or contributions paid by participants to the pension fund. By using the unit credit method, the total value of actuarial liabilities at 1/1/2020 is IDR 405,338.5. Then by using the unit credit method, it is projected that the normal cost on 1/1/2019 is IDR 1,071.43. The calculation method on funding aims to ensure that the collected pension plan funds will be sufficient to pay pension benefits to participants when they retire.

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