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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
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Search results for , issue "Vol 1, No 1 (2020)" : 5 Documents clear
Estimation of the Value-at-Risk (VaR) Using the TARCH Model by Considering the Effects of Long Memory in Stock Investments Nurfadhlina Abdul Halim; Agus Supriatna; Adhy Prasetyo
Operations Research: International Conference Series Vol 1, No 1 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i1.22

Abstract

Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR. Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.
Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices Sukono Sukono; Emah Suryamah; Fujika Novinta S
Operations Research: International Conference Series Vol 1, No 1 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i1.21

Abstract

Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.
Calculation of Value-at-Risk Variance-Covariance with the Approach of Simple Cash Portfolio, Factor Models and Cash Flow Puspa Liza Ghazali; Riaman Riaman; Ristifani Ulfatmi
Operations Research: International Conference Series Vol 1, No 1 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i1.20

Abstract

One way to calculate Value-at-Risk (VaR) is the variation-covariance method. The calculation of VaR covariance assumes stock data is normally distributed. The data needed to calculate VaR by the variance-covariance method is the covariance matrix of Bank Danamon and Bank Mandiri stock data. The main topics discussed in this paper are calculating VaR covariance with a simple cash portfolio approach, factor models and cash flow. For comparison of the use of the three approaches Backtesting, the backtest results indicate that the factor model is the best method.  
Value-at-Risk Estimation Method Based on Normal Distribution, Logistics Distribution and Historical Simulation Dwi Susanti; Sukono Sukono; Maria Jatu Verrany
Operations Research: International Conference Series Vol 1, No 1 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i1.19

Abstract

This paper discusses the risk analysis of single stock and portfolio returns. The stock data analyzed are BNI, BRI shares and portfolio. After obtaining a stock return, value at risk (VaR) will be estimated using the normal distribution approach, logistic distribution, and historical simulation. From the VaR results, a backtest is then conducted to test the validity of the model and the backtest results for BNI and the portfolio produce a smaller QPS on the historical simulation method compared to the normal distribution and logistics distribution approaches. This shows that BNI VaR and VaR portfolios with the historical simulation method are more consistent than other methods. While the backtest results for BRI produced the smallest QPS on the normal distribution approach compared to the logistical distribution and historical simulation approaches. This shows that the VaR BRI using the normal distribution approach is more consistent than the other methods.
Analysis of the Aggregate Heuristic Planning for Planning and Controlling the Amount of Production to Minimize Costs Riana Magdalena
Operations Research: International Conference Series Vol 1, No 1 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i1.18

Abstract

PT.XYZ is one of the companies engaged in the automotive manufacturing industy, where it produces spare parts for cars, motorcycles and trucks. Along with marketing and producing the products, PT.XYZ continues to implement customer satisfaction and the quality of spare parts produced.  The company must anticipate the possibility of production capacity limitations; this must be done as well as possible at the minimum cost. For that, the aggregate heuristic planning is proposed for planning the establishment of a level for production capacity to meet the level of demand obtained from orders with the aim of minimizing total production costs. Aggregate Planning is a process of determining the level of overall production capacity to meet the level of demand obtained from forecasting and order with the aim of minimizing the total cost of production. In this study, three heuristic methods were tried, namely labor control method, subcontracting mixed method, and overtime mixed methods. Based on the results of the study it is known that the subcontracting mixed method is the best method with total aggregate cost of IDR 3,080,689,770, then the labor control method with a total of aggregate cost of IDR 3,080,798,198 and the overtime  mixed  method, with a total  aggregate cost of  IDR 3,081,815,315. 

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