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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 125 Documents
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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.
Value-at-Risk Estimation of Indofood (ICBP) and Gas Company (PGAS) Stocks Using the ARMA-GJR-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.183

Abstract

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.
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.
Prediction of the Number of Visitors to Tourism Objects in the Ujung Genteng Coastal Area of Sukabumi Using the Holt-Winter Method Salamiah, Mia; Sukono, Sukono; Djauhari, Eddy
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.184

Abstract

Ujung Genteng Sukabumi Beach is one of the tourism destinations in Sukabumi Regency, West Java. Forecasting tourist arrivals is a very important factor for tourist destination policies and contributes to the regional economy and the surrounding community. The purpose of this study is to predict the number of tourists who come to Ujung Genteng Beach, Sukabumi. The method used is the Holt-Winter approach exponential smoothing. The Holt-Winter method is used for data that is not stationary, has both trend and seasonal elements. The Holt-Winters method has two models, namely the Additive model and the Multiplicative model. The data used is visitor data in January 2017 - February 2020, the results of the analysis show that the prediction of the number of visitors to Ujung Genteng beach in March 2020 from the additive model is 300 people with a MAPE value of 85.48% and an MSE value of 31230672.68 and a prediction of the number of beach visitors. Ujung Genteng in March 2020 from a multiplicative model of 740 people, with MAPE and MSE values obtained were 86.34% and 27754873.34.
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 Farming Business Insurance Premium Prices with the Variance Premium Principle and Standard Deviation Premium Principle Methods Pramujati, Windya Harieska
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.180

Abstract

The agricultural sector is one of the most important economic sectors in Indonesia, however, agricultural business can also pose a risk of loss resulting in a decrease in agricultural production. This is caused by several factors such as plant pests, weeds, and rainfall. Therefore it is necessary to make an effort to reduce the risk of losses that occur, one of which is by implementing an insurance policy. The risk experienced by farmers is assumed to be a random variable that has a certain distribution. So that the calculation of this risk is related to the probability model, one of which is the aggregate loss model. Then applied the principle of variance premium, and standard deviation premium to calculate the amount of insurance premiums. The amount of premium generated for each of these principles is 2,396,277 and 2,012,839. So it can be said that with the same risk, the standard deviation premium principle produces a premium price that is more economical than the variance premium principle. So that if this principle is applied, farmers will benefit more if they insure their agricultural businesses.
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.
Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model Hidayana, Rizki Apriva; Napitupulu, Herlina; Sukono, Sukono
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.181

Abstract

Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.
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.
Analysis of Changes in Green Land Cover of North Minahasa Gold Mine With Landsat 8 Images using the Normalized Difference Vegetation Index Bahat, Feni; Weku, Winsy; Montolalu, Chriestie
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.182

Abstract

Mining is an activity of extracting non-renewable natural resources, including coal, whose management cannot be separated from the company. In realizing mining activities, they must be managed optimally, so it is necessary to supervise and monitor their activities effectively and efficiently. North Minahasa Mining Toka Tindung is a gold mine that has been operating since 2009, with the first gold production in 2011, and has gold reserves of 122 tons at the end of 2020. Toka Tindung has a mining area of 8,986 hectares (400 thousand square km), or 1.3 percent of the planned contract of work which is 741,000 hectares. This research was conducted by monitoring mining land cover using remote sensing technology based on Landsat 8 satellite imagery. related to vegetation. NDVI has a range of values between -1 to +1, the results of the transformation have different percentages of land use. The greater or positive the NDVI value, the better the vegetation density in the area. This study aims to analyze changes in green land cover in the mining area of North Minahasa in 2013 to 2021 based on variations in the greenness of the vegetation index. The results of the study obtained that Variations in the greenery index of vegetation ranged from 0.0 - 0.4 in 2013 and -0.2 - 0.6 in 2021. Where the mining area environment in 2013 had a vegetation class in the form of rocks, vacant land, meadows, shrubs and dense forests and in 2021 had a vegetation class in the form of rocks, vacant land, grasslands, shrubs, dense forests and water. In 2021 it has a vegetation value of -0.2 whose vegetation class is water due to the loss of Ground Cover Vegetation due to digging too deep to form ponds. on the ground surface. Thus the level of vegetation density in the mining area of North Minahasa has changed from 2013 to 2021. The area without vegetation has generally increased. Replacing the green area and the area with vegetation cover, dense green land cover has decreased.

Page 5 of 13 | Total Record : 125

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