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Operations Research: International Conference Series

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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 6 Documents

Search results for , issue "Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024" : 6 Documents clear

Training on Basic Mathematics for 12th Grade Students of SMA Pasundan Majalaya in Preparation for the 2024 SNBT Hidayana, Rizki Apriva; Yuningsih, Siti Hadiaty; Syarifudin, Abdul Gazir; Amelia, Rika; Nurkholipah, Nenden Siti
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.316

Basic math training plays an important role in preparing students for the National Selection Based Test (SNBT), which is one of the entry pathways to public universities in Indonesia. This study aims to evaluate the effectiveness of academic ability test training in improving the readiness of XII grade students of Pasundan Majalaya High School to face SNBT 2024. The research method used is descriptive quantitative with a case study approach. The study population was all XII grade students of Pasundan Majalaya High School who participated in the training program. Data were collected through observations and tests conducted before and after the training. Data analysis was conducted to measure the improvement of students' academic ability and readiness. The results showed that the academic proficiency test training implemented at Pasundan Majalaya High School was effective in improving students' pre and post test results. There was a significant increase in proficiency test scores through pre and post test results. In addition, the training also helped students in developing time management skills, problem solving strategies, and critical thinking skills. The findings suggest that structured and comprehensive training can significantly improve students' academic readiness, thus helping them to face SNBT more confidently and competitively. This research is expected to contribute to the preparation of Pasundan Majalaya High School students for college entrance selection.

Aggregate Loss Models to Calculate Risk Measures Rahmawati, Septi; Adib, Andhita Zahira; Rusyn, Volodymyr
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.314

The concept of aggregate loss models pertains to a stochastic variable representing the total sum of all losses encountered within a set of insurance policies. In the non-life insurance sector, it is employed to assess the potential losses that an insurance company may face when claims made by policyholders exceed the allocated claim reserves. The purpose of studying aggregate loss models is to ascertain risk measures such as standard deviation of premium principles, value at risk (VaR), and conditional tail expectation (CTE). These steps aid insurance companies in the management and quantification of risks associated with aggregate losses. The standard deviation of premium principles is calculated analytically by substituting expected values and variances, while VaR is estimated using the Monte Carlo method to determine quantile values and confidence intervals. CTE is evaluated by computing the average losses that surpass the VaR threshold. These distributions and parameters require the Pareto distribution, which characterizes claim sizes, and the Poisson or Negative Binomial distribution, which factors in the number of claims. It is crucial to carefully consider the selection of the appropriate distribution, as it plays a significant role in determining the accuracy and reliability of the model. Furthermore, other influencing factors, such as loading factors and confidence intervals, should also be taken into account. These factors have the potential to significantly impact the quantification of risk arising from the model.

Estimated Value-at-Risk Using the ARIMA-GJR-GARCH Model on BBNI Stock Hidayana, Rizki Apriva; Napitupulu, Herlina; Sukono, Sukono
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.317

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 BBNI Shares using the ARIMA-GJR-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the model that will be used, namely the Autoregressive Integrated Moving Average (ARIMA)-Glosten Jagannathan Runkle - generalized autoregressive conditional heteroscedastic (GJR-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 GJR-GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBNI shares. The results obtained are the ARIMA(1,0,1)-GJR-GARCH(1.1) model and a significance level of 5% obtained value-at-risk of 0.0705.

Risk of Ruin (ROR) Analysis in Casino Games Using Poisson Distribution Josua, Lancelot Julsen; Prawiro, Meivin Mulyo; Saputra, Jumadil; Yuningsih, Siti Hadiaty
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.312

Gambling in casino games is an uncertain business because it creates two possibilities between the hope of winning or the risk of losing. The risks faced by casinos are usually analyzed using the Risk of Ruin (ROR). The main focus of this study is to apply the mathematical model of ROR using the Poisson distribution to model random events in gambling by considering the house advantage (a) and the law of large numbers. This study discusses the relationship between variables, such as maximum bet limits and cash flows and examines how these factors affect the risk of casino bankruptcy. In its business characteristics, casinos operate as gambling business entities and utilize the house advantage to achieve their financial benefits. House advantage indicates the profitability of the casino. However, the uncertainty of this gambling can pose a risk of bankruptcy for them. In this study, the house advantage is included in our model for several popular casino games. In addition, a set of full-range scales is defined to facilitate effective assessment of the level of risk faced by the casino, considering its regulatory context. This study also uses the binomial random walk model to describe the race between the casino and the gambler, where each step has two possible outcomes, namely winning or losing. The results of this study are expected to provide insight into the risk in calculating risk in optimizing betting decisions and reducing the risk of bankruptcy.

Implementing the Variance-Covariance Method for Assessing Market Transaction Risks in Raw Material Sector Stocks Kisti, Vuji Annisa; Haq, Fadiah Hasna Nadiatul; Hidayana, Rizki Apriva
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.310

The capital market plays a crucial role in supporting a country's economic growth. Besides being a funding source, the capital market also serves as an investment avenue for investors, particularly through stocks. Every investor must be willing to bear risks in line with their targeted returns. Risk is defined as the uncertainty of future outcomes due to market condition changes, and VaR (Value at Risk) is used to determine the tolerated loss at a certain confidence level. This study discusses the application of the Value at Risk (VaR) method using the Variance-Covariance approach to mitigate market risks in the portfolio of raw material sector stocks. The study focuses on two raw material sector stocks in Indonesia, assuming a normal distribution of asset price changes. The measurement results indicate that with an investment of Rp. 100,000,000.00, a 95% confidence level, and a 1-day period, the VaR of the portfolio of these five stocks is Rp. 2,769,750.00. This research provides critical insights to assist investors in understanding and managing portfolio risks, making VaR a key indicator to measure potential future risks and laying the foundation for decision-making in risk management.

Determination of Micro-Insurance Premium Price for Fisheries Using Poisson-Exponential Aggregate Distribution Approach Fadhilah, Dila Nur; Syahla, Raynita; Azahra, Astrid Sulistya
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.315

Engaging in pond aquaculture is currently an attractive option amid the high demand for fish in the market. Entrepreneurial opportunities in the pond fish farming sector are increasingly open, although the risk of crop failure remains, both due to weather factors and the livestock process. Crop failure can have a significant financial impact on pond aquaculture cultivators. Therefore, it is necessary to have special insurance to protect against financial losses due to risks that can occur, namely Fisheries Micro Insurance. Microinsurance is a type of insurance product that is specifically designed for low-income people, offering simple, accessible, economically priced features and administration, and a fast compensation settlement process. The focus of this research is to calculate premium prices by applying an aggregate risk model approach. The data used is the number of incidents and the amount of losses due to crop failure in shrimp pond cultivation in Pandeglang Regency in the period of January 1, 2019-January 1, 2021. Data on the number of events follows the Poisson distribution, while data on the magnitude of losses follows the Exponential distribution. Furthermore, the Maximum Likelihood Estimation (MLE) method is used to calculate the parameter estimation. The average and variance estimates of the aggregate risk are used to determine the amount of premiums. The result of the premium selection in this study is IDR 42,005,600. The amount of premium reflects the collective premium resulting from the calculation based on the standard deviation principle.

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Editorial Address Jl. Merkuri Timur VI No. 1, RT. 007, RW. 004, Manjahlega, Rancasari, Kota Bandung, Jawa Barat, INDONESIA

Location Kota bandung, Jawa barat INDONESIA

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Contact Name
-

Contact Email
orics.iorajournal@gmail.com

Phone
+6285841953112

Journal Mail Official
orics.iorajournal@gmail.com

Editorial Address
Jl. Merkuri Timur VI No. 1, RT. 007, RW. 004, Manjahlega, Rancasari, Kota Bandung, Jawa Barat, INDONESIA

Location
Kota bandung,

Jawa barat

INDONESIA