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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 78 Documents
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Determination of Deposit Insurance Premium (LPS): Merton’s Option Theory with Co-Insurance Consideration Naomi Pandiangan
Operations Research: International Conference Series Vol 1, No 2 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i2.24

Abstract

Indonesia is a developing country that implements a deposit insurance system. Deposit Insurance Agency or LPS is an Indonesian deposit insurance established in 2004, which is Indonesian still unfamiliar with LPS, both among researchers, and the general public. Almost all deposit insurance in every country including Indonesia has the same problem, the problem is how to calculate premiums and how to avoid moral hazard by banks, therefore in this study will discuss how to determine premiums from the development of Black-Scholes option theory (1973) conducted by Merton (1977). To prevent banks from engaging in moral hazard, co-insurance is considered in this study, which is banks take the risk to anticipate 'excessive risk-taking' behavior. that occurs if the value of the asset is smaller than value of the deposit after joining the insurance program. So it is expected to encourage banks to beware.
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.
Identification of Pathogenic Bacteria on Carp Commodities (Cyprinus carpio) at Quality Control and Fishery Product Safety Agency (BKIPM) of Bengkulu Risky Hadi Wibowo
Operations Research: International Conference Series Vol 1, No 2 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i2.72

Abstract

Carp (Cyprinus carpio) is a type of freshwater fish that is widely cultivated. The increase in the amount of production and trade in freshwater fishery commodities both for consumption in Bengkulu will potentially increase the risk of entry and spread of pests and diseases in fish, which at the same time will be a threat that can endanger and damage the sustainability of fishery biological resources. Bacteria that infect fish can inhibit the expected production targets, which is an outbreak of pathogenic fish disease caused by bacteria. This study aims to identify pathogenic bacteria that infect Carp (C. carpio). Carp were obtained from fish traders at Panorama Market, Bengkulu City. Carp samples were selected based on clinical symptoms that were no longer healthy. Isolation of bacteria from Carp’s organs using Triptic Soy Agar (TSA) media. The isolates were screened by morphological characters and biochemical test. The results of this study showed that total of 2 bacteria were isolated. Based on biochemical tests carried out such as the Simmons Citrate test, Triple Sugar Iron Agar, Oxidative-Fermentative, Motility Indol Ornithine, Lysine Iron Agar, MR-VP, urea, catalase, oxidase, gelatin, confectionery test, and Rimmler-Shotts test, pathogenic isolates Sp 1. in the sample have a close relationship with Plesiomonas shigelloides while the pathogenic isolates Sp 2. and Sp 3. have a close relationship with Aeromonas hydrophila.
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.
Student’s Worksheet Practically by Using Discovery Learning Model to the Ability of Understanding Concept and Mathematics Problem Solving at Grade VII Grade of SMPN 38 Padang Teni Suriani; Dewi Devita
Operations Research: International Conference Series Vol 1, No 2 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i2.73

Abstract

The ability of understanding mathematical concept and problem-solving mathematics is one of aims of mathematic subject it is expected to develop in mathematics learning. Based on the observation that was doing by students of VII grade SMPN 38 Padang in 2019/2020 academic years, this research obtain that the students got difficulty in doing story problem that related in real life situation without illustration’s picture. It causes, mathematics learning still focused on text book and LKPD. LKPD that SMPN 38 Padang has still less variation and there is no step to find out and construct by their own learning concept. One of learning models that can be developed is discovery learning. Discovery learning is a learning model that can be informed by discovering process. The aims of this research are to obtain and develop LKPD by using discovery learning that is practicable. Thetype of the research use development research with formative evaluation type that according to Tesmer view. Practicality of LKPD is limited on field test step which giving practical questionnaire to students and teachers. The result of practicality of LKPD from teacher is 85% with very practical category and from the student is 85.30% with very practical category.
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.  
Antibacterial Activities of Pseudomonas orientalis APD 16 Isolate Sponge-Associated Aplysina sp. from Enggano Island Against Escherichia coli and Staphylococcus aureus Risky Hadi Wibowo
Operations Research: International Conference Series Vol 1, No 2 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i2.74

Abstract

Sponge is one of the invertebrates from the Porifera phylum. Sponge bodies have structural complexity with different cell layers. The sponge has many pores (ostium) on the surface of their body as a filter feeder. Sponge is recognized as organisms that have the potential because they can produce metabolites. Secondary metabolites produced by sponges are the result of the association of sponges with bacteria. The sponge used in this study is the Aplysina sp. sponge collected from Enggano island, Bengkulu Province. Aplysina sp. sponge is known to contain metabolites with antibacterial, antifungal and cytotoxic activity on cancer cells. This study aims to identify of potential isolate associated with Aplysina sp. sponge collected from Enggano island. Isolation of bacteria from Aplysina sp. sponge using Sea Water Complete (SWC) media. The isolate was screened by antagonistic test, morphological characters, Gram-staining, biochemical test and molecular identification. Based on the antagonistic test, APD 16 isolate could inhibit Escherichia coli and Staphylococcus aureus in Vitro. APD 16 isolate was identified molecularly using of 16S rRNAgenes analysis and it genetically close with Pseudomonas orientalis.
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. 
Use of ARIMA-GARCH Model to Estimating Value-at-Risk in Gudang Garam (GGRM) Stock Alberto Simanjuntak
Operations Research: International Conference Series Vol 1, No 2 (2020)
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v1i2.75

Abstract

Stocks are one of the best-known forms of investment and are still used today. In stock investment, it is necessary to know the movement and risk of loss that may be obtained from the stock investment, so that investors can consider the possible losses. One way to calculate risk is to use Value-at-Risk (VaR). Since the stock movement is in the form of a time series, a model can be formed to predict the movement of the stock, which can then be used for VaR calculations using time series analysis. The purpose of the study was to determine the Value-at-Risk value of Gudang Garam Tbk.’s (GGRM) shares using time series analysis. The data used for this research is the daily closing price of shares for three years. At the time series analysis stage, the models used in predicting stock movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The average and variance values obtained from the model are then used in calculating the VaR of GGRM shares. Based on the results of the study, it was found that the GGRM stock has a VaR of 0.069598. In other words, if an investment of IDR 1,000,000.00 is made for GGRM shares for 37 days (5% of 747 days), the investment period with a 95% confidence level, the maximum loss that may be borne by the investor is IDR 69,598.00.

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