cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
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
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
 7   8   9   10   11 
Comparison of K-Medoids and Clara Algorithm in Poverty Clustering Analysis in Indonesia Ardini, Ananda Rizki Dwi; Sirait, Haposan
Operations Research: International Conference Series Vol. 4 No. 4 (2023): Operations Research International Conference Series (ORICS), December 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i4.279

Abstract

The Covid-19 pandemic entered Indonesia in March 2020, so the government imposed restrictions on people's movement in various regencies. The imposition of restrictions on people's movement will have an impact on the economy to the point of poverty. Poverty is influenced by several factors such as population, health, education, employment and economic factors. The poverty of a district/city in Indonesia is grouped to assist the government in alleviating poverty more efficiently. The process of grouping data in data mining is to group districts/cities in Indonesia based on factors that affect poverty with the K-Medoids and CLARA algorithms, then compare the two methods based on the average value of the ratio of the standard deviations. The variables used in this study consist of 4 variables, namely human development index (HDI), gross regional domestic product (GRDP), unemployment rate, and population density. The results of this study indicate that using the K-Medoids obtained 2 clusters, while using the CLARA algorithm obtained 3 clusters. Based on the results of grouping the two algorithms, the best algorithm was obtained using cluster validation, namely the CLARA algorithm because it has the average value of the ratio of the smallest standard deviation of 0.106. 
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

Abstract

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.
Exploring Investment Decision-Making with CAPM: Case Studies on Ten Raw Materials Companies Listed in Stock Exchange Haq, Fadiah Hasna Nadiatul; Sukono, Sukono
Operations Research: International Conference Series Vol. 5 No. 1 (2024): Operations Research International Conference Series (ORICS), March 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i1.294

Abstract

The investment business in Indonesia experienced significant growth in line with the increasing stock trading activity in the capital market. The large number of capital markets in Indonesia means investors have to be careful in determining the shares to be chosen. Based on transaction value, the raw materials sector is the second largest sector that supports the Indonesian capital market. Given the large number of issuers in the raw materials sector, determining investment portfolios is important to obtain optimal results. CAPM can classify stocks as efficient or not based on their expected return value. The results obtained can be used as a consideration in portfolio decision-making. This research identifies 10 stocks in the raw materials sector listed on the IDX. Of the 10 stocks studied, 8 are included in the efficient category, which has a greater return than expected, and 2 are included in the inefficient category. This means that investors who want to invest in raw materials can make a decision to buy these 8 stocks, and it is not recommended to buy shares in 2 inefficient category stocks or sell 2 stocks.
Calculation of Motor Vehicle Insurance Premiums Through Evaluation of Claim Frequency and Amount Data Bagariang, Elizabeth Irene; Raharjanti, Amalia
Operations Research: International Conference Series Vol. 5 No. 3 (2024): Operations Research International Conference Series (ORICS), September 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i3.330

Abstract

Insurance, as a risk control strategy by transferring the burden of risk from one party to another, consists of two main forms: life insurance, which covers financial losses from the risk of death of the policyholder, and general insurance, which involves the transfer of risk against property losses. Motor vehicle insurance has become a common product reflecting the high value and benefits of motor vehicles, which has resulted in an increase in vehicle ownership. Although the increase in the number of vehicles contributes to the increase in road accidents, many owners who suffer losses do not receive the compensation they deserve. In this context, the premium becomes a key factor, where the policyholder pays a certain amount of money to get protection. This research aims to apply risk premium calculation based on claim frequency and claim size data, as conducted by Ozgurel in 2005, especially for each vehicle category and region in XYZ insurance company. The main problem is to optimize the premium calculation to reflect the actual risk, providing a more accurate understanding of the influence of vehicle and regional characteristics in determining a fair and appropriate premium.
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

Abstract

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.
Pricing of Fisheries Microinsurance Premiums using the Poisson-Exponential Aggregate Distribution Approach Fadhilah, Dila Nur; Shahla, Raynita
Operations Research: International Conference Series Vol. 4 No. 4 (2023): Operations Research International Conference Series (ORICS), December 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i4.272

Abstract

Engaged in pond aquaculture is currently an attractive choice 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 livestock processes. Crop failure can have a significant financial impact on pond fishery farmers. Therefore, there is a need for special insurance to protect against financial losses due to risks that can occur, namely Micro Fisheries Insurance. Microinsurance is a type of insurance product specifically designed for people with low income levels, offers features and administration that is simple, easily accessible, has an economical price, and a fast compensation settlement process. The focus of this study is to calculate premium prices by applying an aggregate risk model approach. The data used are the number of events and the magnitude of losses due to crop failure in shrimp pond cultivation in Pandeglang Regency in the period January 1, 2019-January 1, 2021. Data on the number of events follow the Poisson distribution, while data on the magnitude of losses follow the Exponential distribution. Next, it uses the Maximum Likelihood Estimation (MLE) method to calculate parameter estimation. The average and variance of aggregate risk is used to determine the size of the premium. The premium selection results in this study amounted to Rp42,005,600. The amount of the premium reflects the collective premium resulting from the calculation based on the standard deviation principle.
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

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 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.
Developing Financial Intelligence With Financial Mathematics Gunawan, Chairamanda Binar; Jannah, Miftahul
Operations Research: International Conference Series Vol. 5 No. 1 (2024): Operations Research International Conference Series (ORICS), March 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i1.297

Abstract

Financial intelligence is an individual's ability to understand, manage, and optimize the management of personal finances. Financial mathematics is becoming an important tool in developing financial intelligence. In this research using literature study method this method involves the search and collection of information from related literature sources. Through financial mathematics, one can calculate the future value of an investment, estimate the return on an investment, calculate annuity payments, understand the concept of time value of money, and analyze investment risk. The application of financial mathematics in financial intelligence provides an advantage in taking better financial decisions and managing finances effectively. By utilizing financial mathematics, one can improve their understanding of Finance, make effective financial planning, and take better financial decisions.
Forecasting Indonesian Stock Index Using ARMA-GARCH Model Susanti, Dwi; Labitta, Kirana Fara; Sukono, Sukono
Operations Research: International Conference Series Vol. 5 No. 3 (2024): Operations Research International Conference Series (ORICS), September 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i3.328

Abstract

The stock market is an institution that provides a facility for buying and selling stocks. Covid-19 is an issue that has affected the stock markets of many countries, including Indonesia. Due to the pandemic, the condition of stocks before and during Covid-19 is certainly different. Stocks can be measured using stock indices. To predict future stock conditions, it is necessary to forecast the stock index. Therefore, this research aims to forecast the Indonesian stock index before and during Covid-19 using the ARMA-GARCH time series model. The results show that the best forecasting model for before Covid-19 data is ARMA(0,2)-GARCH(1,0), and for the data during Covid-19, it is ARMA(3,3)-GARCH(3,3). These findings can help investors make better investment decisions in the future.
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

Abstract

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.

Page 9 of 13 | Total Record : 125

 7   8   9   10   11 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 2 (2025): Operations Research International Conference Series (ORICS), June 2025 Vol. 6 No. 1 (2025): Operations Research International Conference Series (ORICS), March 2025 Vol. 6 No. 3 (2025): Operations Research: International Conference Series (ORICS) Vol. 5 No. 4 (2024): Operations Research International Conference Series (ORICS), December 2024 Vol. 5 No. 3 (2024): Operations Research International Conference Series (ORICS), September 2024 Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024 Vol. 5 No. 1 (2024): Operations Research International Conference Series (ORICS), March 2024 Vol. 4 No. 4 (2023): Operations Research International Conference Series (ORICS), December 2023 Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023 Vol. 4 No. 2 (2023): Operations Research International Conference Series (ORICS), June 2023 Vol. 4 No. 1 (2023): Operations Research International Conference Series (ORICS), March 2023 Vol. 3 No. 4 (2022): Operations Research International Conference Series (ORICS), December 2022 Vol. 3 No. 3 (2022): Operations Research International Conference Series (ORICS), September 2022 Vol. 3 No. 2 (2022): Operations Research International Conference Series (ORICS), June 2022 Vol. 3 No. 1 (2022): Operations Research International Conference Series (ORICS), March 2022 Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021 Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021 Vol. 2 No. 2 (2021): Operations Research International Conference Series (ORICS), June 2021 Vol. 2 No. 1 (2021): Operations Research International Conference Series (ORICS), March 2021 Vol. 1 No. 4 (2020): Operations Research International Conference Series (ORICS), December 2020 Vol. 1 No. 3 (2020): Operations Research International Conference Series (ORICS), September 2020 Vol. 1 No. 2 (2020): Operations Research International Conference Series (ORICS), June 2020 Vol. 1 No. 1 (2020): Operations Research International Conference Series (ORICS), March 2020 Vol 1, No 2 (2020) More Issue