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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 6 Documents
 1 
Search results for , issue "Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023" : 6 Documents clear
Determining The Optimal Portfolio Markowitz Model on Moving Stock Prices Using Brown Motion Geometry Suhariman, Fikrianto; Prabowo, Agung; Wardayani, Ari
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.253

Abstract

Stock price movements that fluctuate and follow a stochastic process will make it difficult for investors to start investing. For that, we need a stochastic mathematical model. The Brownian geometry motion model is one of the stochastic models that can be used to display the condition of a stock's price movement. Investment is related to the rate of return (return) and the risk obtained. The higher the rate of return obtained, the higher the risk obtained. Therefore, a portfolio calculation is needed, one of which is using the Markowitz model. The Markowitz model can be used to determine the optimal portfolio. The purpose of this study is to model stock prices and form an optimal portfolio. The stock price data used are BBRI, TLKM, and ADRO for the period from 1 July 2021 to 31 August 2022. The results obtained from this study are that there are three portfolio preferences. If investors like high risk to get high returns, then the combined allocation of funds for BBRI, TLKM and ADRO shares is 10.51%, 42.05% and 47.44% respectively. If investors do not like high risk but still want to get a return that is balanced with risk, then the combination of fund allocation for shares of BBRI, TLKM and ADRO is 22.62%, 46.63% and 30.75%, respectively. If the investor chooses minimum risk, the combined allocation of BBRI, TLKM and ADRO shares is 34.73%, 51.21% and 14.06%, respectively.
ESTIMATED AVERAGE TIME TO HIRE EMPLOYEES OF THE COMPANY BASED ON KUMARASWAMY DISTRIBUTION Usraini, Laisa
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.249

Abstract

Recruitment of employees in a company is a process starting from selecting and receiving prospective employees, in order to find workers who are able to work in a company. However, companies must know when recruitment will open. The purpose of this research is to find out how long the average opportunity time of recruitment is. The method used is the Kumaraswamy distribution with three parameters as benchmarks for the estimated recruitment time, in order to find out how long the chances of the recruitment lasting using a simplified survival function using the properties of the Laplace transform. Based on the estimation of the average time of recruitment, the results show that the greater the value of the parameters the less chance the average recruitment time is or the tighter the prospective employees are accepted.
Food Sector Stock Investment Portfolio Optimization using Mean-Expected Shortfall Model with Particle Swarm Optimization Tampubolon, Carlos Naek Tua; Subartini, Betty; Sukono, Sukono
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.252

Abstract

One of the most promising investment products is stocks. Stocks have great profit potential, but the risks associated with this investment should not be ignored by investors. Therefore, an optimal investment strategy is needed by forming an investment portfolio, in order to minimize risk and maximize profits that can be obtained. This study aims to optimize the investment portfolio. The method used in this research is based on the Mean-Expected Shortfall (Mean-ES) model. The use of this method is expected that investors can get a more accurate picture of the level of risk associated with their stock portfolio. In addition, Particle Swarm Optimization (PSO) can also be used to optimize the allocation of funds in a stock portfolio.  Applying PSO, investors can find the optimal combination of fund allocation to achieve a high level of return. Based on the results of the analysis conducted on the following five stocks AALI, BISI, DSNG, LSIP and SMAR, the results show a risk level of 0.0014 and a return level of 0.021%.  Thus, investors can form a stock portfolio that has a high potential return, while minimizing the risks associated with stock investment. The implementation of this optimal investment strategy can assist investors in achieving their financial goals in a more effective manner.  Considering the potential returns and risks involved, investors can make wiser investment decisions and optimize the performance of their stock portfolio.
Mean-Variance Investment Without Risk-Free Assets in PT Company Shares PT Ace Hardware (Aces.Jk), PT Mayora Indah (Myor.Jk), PT Bri (Bbri.Jk), PT Siloam Hospital (Silo.Jk), PT Eterindo Wahanatama (Etwa.Jk) Raharjanti, Amalia
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.251

Abstract

Portfolio is a form of strategy that investors often apply in risky investment conditions. The essence of portfolio construction is to allocate funds to various investment options to minimize investment risk. Therefore, the aim of this discussion is to construct an investment portfolio of several shares using an average variable portfolio optimization model without risk-free assets. To obtain an optimal portfolio, a mean-variance investment optimization model without risk-free assets or what is called the Basic Markowitz model is used. This involves investors measuring the risk of an asset using its “variance” and then comparing it to the asset's average. It is hoped that this discussion can help investors to obtain an optimal portfolio, especially from the five selected shares. 
SELECTION OF THE BEST B-SPLINE REGRESSION MODEL FOR ESTIMATING BITCOIN PRICE INCREASES BASED ON ORDER AND OPTIMAL KNOT POINT Faridza, Mohammad Dandi
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.248

Abstract

The cryptographic virtual currency, bitcoin, is considered the main originator of cryptocurrencies that emerged due United States financial crisis in 2008. The idea was sparked by Nakamoto by introducing an alternative currency system that really refers to the strength of supply and demand. Based on INDODAX data, the bitcoin exchange rate during October 2020 to February 2021 is a condition of a large increase in a short time with a percentage increase of 450%. The increase in bitcoin prices can be modelled using the b-spline nonparametric regression method based on order and optimal knot points based on the smallest Generalized Cross Validation value. The resulting b-spline 4 degree and the number of knots points 5 as the best model with each bases described recursively.
Ensemble Rock Application for Classification of Slb in Riau Province Based on Infrastructure Facilities Minallah, Munzhiroh Rizki
Operations Research: International Conference Series Vol. 4 No. 3 (2023): Operations Research International Conference Series (ORICS), September 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i3.250

Abstract

The 2020 school participation figure states that 20.56% of children in the disability category have the status of not/never been to school (BPS, 2020). This shows that there are still many children with disabilities who have not received adequate education. Therefore, attention to the availability of facilities and access to education for children with disabilities needs to be increased so that there is no inequality of school participation between children with disabilities and non-disabled children. On extraordinary school data statistical methods can be applied for various purposes. The method that can be used to group mixed-type data is ensemble. In this study, the ensemble ROCK (Robust Clustering using links) method was used at 47 extraordinary schools in Riau Province. Using the value 𝜃 of 0.22 in the ROCK ensemble method, we get 3 optimal clusters with a ratio of 0.08177794. It was found that cluster 3 is a cluster that does not have adequate facilities such as a laboratory, library and internet network than other clusters. It can be said that cluster 3 needs more attention than other clusters.

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