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 5 Documents
 1 
Search results for , issue "Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021" : 5 Documents clear
Value-at-Risk Estimation of Indofood (ICBP) and Gas Company (PGAS) Stocks Using the ARMA-GJR-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.183

Abstract

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.
Prediction of the Number of Visitors to Tourism Objects in the Ujung Genteng Coastal Area of Sukabumi Using the Holt-Winter Method Salamiah, Mia; Sukono, Sukono; Djauhari, Eddy
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.184

Abstract

Ujung Genteng Sukabumi Beach is one of the tourism destinations in Sukabumi Regency, West Java. Forecasting tourist arrivals is a very important factor for tourist destination policies and contributes to the regional economy and the surrounding community. The purpose of this study is to predict the number of tourists who come to Ujung Genteng Beach, Sukabumi. The method used is the Holt-Winter approach exponential smoothing. The Holt-Winter method is used for data that is not stationary, has both trend and seasonal elements. The Holt-Winters method has two models, namely the Additive model and the Multiplicative model. The data used is visitor data in January 2017 - February 2020, the results of the analysis show that the prediction of the number of visitors to Ujung Genteng beach in March 2020 from the additive model is 300 people with a MAPE value of 85.48% and an MSE value of 31230672.68 and a prediction of the number of beach visitors. Ujung Genteng in March 2020 from a multiplicative model of 740 people, with MAPE and MSE values obtained were 86.34% and 27754873.34.
Determination of Farming Business Insurance Premium Prices with the Variance Premium Principle and Standard Deviation Premium Principle Methods Pramujati, Windya Harieska
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.180

Abstract

The agricultural sector is one of the most important economic sectors in Indonesia, however, agricultural business can also pose a risk of loss resulting in a decrease in agricultural production. This is caused by several factors such as plant pests, weeds, and rainfall. Therefore it is necessary to make an effort to reduce the risk of losses that occur, one of which is by implementing an insurance policy. The risk experienced by farmers is assumed to be a random variable that has a certain distribution. So that the calculation of this risk is related to the probability model, one of which is the aggregate loss model. Then applied the principle of variance premium, and standard deviation premium to calculate the amount of insurance premiums. The amount of premium generated for each of these principles is 2,396,277 and 2,012,839. So it can be said that with the same risk, the standard deviation premium principle produces a premium price that is more economical than the variance premium principle. So that if this principle is applied, farmers will benefit more if they insure their agricultural businesses.
Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model Hidayana, Rizki Apriva; Napitupulu, Herlina; Sukono, Sukono
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.181

Abstract

Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.
Analysis of Changes in Green Land Cover of North Minahasa Gold Mine With Landsat 8 Images using the Normalized Difference Vegetation Index Bahat, Feni; Weku, Winsy; Montolalu, Chriestie
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.182

Abstract

Mining is an activity of extracting non-renewable natural resources, including coal, whose management cannot be separated from the company. In realizing mining activities, they must be managed optimally, so it is necessary to supervise and monitor their activities effectively and efficiently. North Minahasa Mining Toka Tindung is a gold mine that has been operating since 2009, with the first gold production in 2011, and has gold reserves of 122 tons at the end of 2020. Toka Tindung has a mining area of 8,986 hectares (400 thousand square km), or 1.3 percent of the planned contract of work which is 741,000 hectares. This research was conducted by monitoring mining land cover using remote sensing technology based on Landsat 8 satellite imagery. related to vegetation. NDVI has a range of values between -1 to +1, the results of the transformation have different percentages of land use. The greater or positive the NDVI value, the better the vegetation density in the area. This study aims to analyze changes in green land cover in the mining area of North Minahasa in 2013 to 2021 based on variations in the greenness of the vegetation index. The results of the study obtained that Variations in the greenery index of vegetation ranged from 0.0 - 0.4 in 2013 and -0.2 - 0.6 in 2021. Where the mining area environment in 2013 had a vegetation class in the form of rocks, vacant land, meadows, shrubs and dense forests and in 2021 had a vegetation class in the form of rocks, vacant land, grasslands, shrubs, dense forests and water. In 2021 it has a vegetation value of -0.2 whose vegetation class is water due to the loss of Ground Cover Vegetation due to digging too deep to form ponds. on the ground surface. Thus the level of vegetation density in the mining area of North Minahasa has changed from 2013 to 2021. The area without vegetation has generally increased. Replacing the green area and the area with vegetation cover, dense green land cover has decreased.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol. 6 No. 4 (2025): Operations Research International Conference Series (ORICS), December 2025 Vol. 6 No. 3 (2025): Operations Research: International Conference Series (ORICS) September 2025 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. 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