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International Journal of Global Operations Research

ijgor
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Published by Indonesian Operations Research Association

ISSN : 27231747 EISSN : 27221016 DOI : https://doi.org/10.47194/ijgor

Core Subject : Economy, Science, Education, Engineering,

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

International Journal of Global Operations Research (IJGOR) is published 4 times a year and is the flagship journal of the Indonesian Operational Research Association (IORA). It is the aim of IJGOR 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. Topics Covered: Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support System Applied Operations Research in Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Applied Operations Research inGovernment Heuristics Industrial Engineering Information Management Information Technology Inventory Management Knowledge Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Applied Operations Research in Marketing Engineering Markov Chains Mathematics Actuarial Sciences Military and Homeland Security Networks Operations Management Organizational Behavior Planning and Scheduling Policy Modeling and Public Sector Applied Operations Research inPolitical Science Production Management Applied Operations Research inPsychology Queuing Theory Revenue & Risk Management Services management Simulation Applied Operations Research inSociology Applied Operations Research inSports Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation And so on

Arjuna Subject : Umum - Umum

Articles 164 Documents

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Game Theory as a Marketing Optimization Tool: A Case Study on Kelom Geulis Azahra, Astrid Sulistya; Saefullah, Rifki; Wahid, Alim Jaizul
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.340

Competition in the market for traditional art products, such as Kelom Geulis, has become increasingly intense along with the growing public interest in aesthetically and culturally valuable items. This competition forces producers to develop effective marketing strategies to maintain their competitiveness. This study adopts a game theory approach to evaluate and formulate optimal marketing strategies between two major producers. The research method involves the use of questionnaires covering three main aspects: improving product quality, setting competitive prices, and enhancing customer service. Data analysis is conducted using a payoff matrix to determine the best strategies that can increase profits or reduce losses for each party.The results show that a saddle point is reached at a value of 4.57, where PT A achieves a profit increase from 4 to 4.57, while PT B reduces its loss from 6 to 4.57. This optimal strategy can be achieved if PT A prioritizes improving product quality and setting competitive prices, while PT B A prioritizes setting competitive prices and service quality enhancement. The implementation of these strategies has proven effective in strengthening the competitiveness of Kelom Geulis in the market. This study is expected to serve as a practical reference for Kelom Geulis producers to continuously adapt their marketing strategies, ensuring their relevance in the market and appealing to consumers

Analysis of Queueing Systems in Fast Food Restaurants Using the M/M/c Model: A Case Study during Peak Hours Hidayana, Rizki Apriva; Yohandoko, Setyo Luthfi Okta
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.341

This study evaluates the queueing system of a fast-food restaurant using the M/M/c model to optimize the number of service counters (servers) for reducing customer waiting times during peak hours. The analysis involved simulating different configurations with 1, 2, and 3 servers, considering a customer arrival rate of 20 customers per minute and a service rate of 25 customers per minute. Results demonstrate a clear relationship between the number of servers and system performance. A single-server system resulted in an average total time of 12 seconds per customer in the system, highlighting significant delays during peak times. Introducing a second server reduced the average waiting time in the system to 4.44 seconds, striking an effective balance between service efficiency and resource utilization. However, adding a third server showed minimal improvement, as the system's utility ratio declined significantly, suggesting underutilized resources. Based on these findings, a two-server configuration is identified as the optimal solution, efficiently managing the customer arrival rate while maintaining a balanced utility ratio. This study emphasizes the practical value of combining queueing models and simulations to improve operational efficiency in fast-food service systems. The insights can guide decision-making processes for restaurant managers aiming to enhance customer satisfaction and optimize resource allocation during high-demand periods.

Modeling of COVID-19 Growth Cases in Bandung Regency and Bandung City Using Vector Autoregression Megantara, Tubagus Robbi; Hidayana, Rizki Apriva; Syarifudin, Abdul Gazir; Amelia, Rika; Nurkholipah, Nenden Siti
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.342

COVID-19 is a global health epidemic due to increasing infections and deaths. Indonesia has many confirmed cases with high daily case growth, including the Bandung City and Bandung Regency areas. High mobility between regions can impact the growth of COVID-19 cases. Strategies to prevent the growth of COVID-19 cases need to be carried out by considering the growth of COVID-19 cases in the nearest area. The Vector Autoregression (VAR) model is a forecasting model that can consider geographic impacts. This study aims to model the growth of cases in adjacent areas and have high mobility using the VAR method. The growth of COVID-19 cases in Bandung City and Bandung Regency is integrated into the VAR model to see the impact of each other. The VAR model also considers the impact of case growth in the past on its region's future. Transformation and differencing are carried out on the time series of case growth in each region to achieve time-series stationarity so that the VAR model can be carried out. First-order VAR becomes a model representing the growth of COVID-19 cases in Bandung City and Bandung Regency. The model shows that COVID-19 cases in each region will decrease over time and each region impacts each other. Decreasing cases growth can be caused because people who have been infected and vaccinated have sound immune systems to prevent re-infection. However, prevention still needs to be done to stop the pandemic. Therefore, restrictions on mobility between regions can be used as a strategy to prevent COVID-19 infection.

Sentiment Analysis of Tiktok App Reviews on Google Play using Several Machine Learning Methods Suhaimi, Nurnisaa binti Abdullah; Lestari, Mugi
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.343

Sentiment analysis has become increasingly important in understanding user perceptions of digital platforms. This study focuses on analyzing TikTok application reviews from the Google Play Store in Indonesia using machine learning techniques. The research aims to investigate sentiment distribution and compare the performance of three popular machine learning models: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The study employed a comprehensive methodology involving data collection, preprocessing, feature extraction, and model evaluation. A dataset of 10,000 TikTok reviews was collected and preprocessed using techniques such as case folding, tokenization, and stopword removal. The sentiment labeling process categorizes reviews into positive, negative, and neutral sentiments based on user ratings. The TF-IDF algorithm was used for feature extraction, and the SMOTE technique addressed class imbalance. Results revealed a predominance of negative sentiment (53.5%), followed by neutral (32.1%) and positive (14.4%) sentiment. Model performance comparisons at different data sharing ratios (80/20 and 70/30) demonstrated that Random Forest and SVM consistently outperformed Naive Bayes. At the 80/20 ratio, Random Forest achieved the highest accuracy of 83.73%, highlighting its effectiveness in sentiment classification. The research contributes to the field of sentiment analysis and natural language processing by providing insights into user experiences with the TikTok application in Indonesia. The findings can guide application developers in understanding user perceptions and improving user experience.

Application of Mathematical Models in Creating Optimal Strategies to Reduce Home Ownership Credit Costs Alifah, Fathin; Kurniasih, Kayla Nurul
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.356

Home Ownership Credit (KPR) is one of the important financing options for people who want to own a home. However, the total amount to be paid during the loan period is high, which is why for many borrowers this study aims to apply mathematical models to develop optimal strategies to reduce the overall mortgage entry costs. This study uses the annuity concept to model installment payments by considering factors such as interest rate, term, and payment frequency. The model also considers repayment scenarios that include early repayment options to reduce long-term interest costs. Simulation results show that by choosing a shorter period and using early payments in certain periods, significant overall cost savings can be achieved. In addition, although variable interest rates are riskier, they have been shown to offer greater savings potential than fixed interest rates under certain economic conditions. The conclusion of this study is that the application of an optimal payment strategy through a mathematical approach can significantly reduce total mortgage costs and provide long-term benefits to borrowers.

Calculation of Passenger Type Car Insurance Based on Frequency Data and Claim Size Wandira, Ika
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.357

Calculation of motor vehicle insurance risk premiums is an important aspect in the insurance industry, which functions to determine the amount of premium to be paid by the policyholder. This study aims to analyze how the calculation of risk premiums is carried out using claim frequency data and claim amounts as the main basis for determining fair premiums and in accordance with the risks faced by insurance companies. Through this calculation model, insurance companies can estimate the amount of risk faced and set proportional premiums. The data used in this study are assumption data that refer to historical claims to provide an overview of accurate and realistic premium calculations in the context of motor vehicle insurance. The results of the calculation of motor vehicle insurance risk premiums with Total Loss Only (TLO) protection are influenced by the frequency and amount of claims. Analysis of claim data for the 2015-2020 period shows an increasing trend in the average claim amount from IDR 8,000,000 to IDR 10,000,000, while the average annual premium is relatively stable at IDR 1,200,000. Risk premiums increase as the number of claims increases, so companies need to allocate sufficient funds to cover these risks. In addition, the increasing frequency of claims each year indicates a higher risk for the company

Extreme Risk Analysis on Financial Derivatives in Indonesia Using Extreme Value-at-Risk Based on Generalized Pareto Distribution (GPD) Febrianty, Popy
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.358

Develop a Value at Risk (VaR) model based on Generalized Pareto Distribution (GPD) to analyze extreme risks in financial derivative portfolios in Indonesia. The GPD approach is chosen because it can describe the tail distribution of price data that exceeds a certain threshold. Price data (in US dollars/USD) of financial derivatives from the Indonesian market are collected from 2011 to 2022 taken from the International Financial Statistics (IMF Data). Furthermore, the data is analyzed to determine the threshold, then the GPD model is applied to extract the tail distribution. The calculation of GPD-based VaR is carried out to provide a more accurate estimate of the potential for extreme losses. This study is expected to contribute to the management of extreme risks in the derivatives market in Indonesia, as well as provide guidance for investors and financial institutions in making more informed investment decisions.

Comparative Analysis of Pension Funds with Single Interest Model and Compound Interest Model Septiandari, Thariza Haifa; Akbar, Muhammad Rizky Umaran
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.359

This study aims to provide a basic analysis of pension fund calculation using a simple interest model. In this study, we assume a fixed contribution invested at a fixed interest rate during the retirement period of the retiree. Using the basic formula of simple interest accumulation, we calculate the final amount available at retirement. The results show that even with low interest rates, wealth accumulation can increase significantly with long-term planning. This study is expected to provide readers with an initial understanding of the importance of pension fund planning.

Interest Comparison on Paylater Schemes: Case Study on E-Commerce Shopee and Tokopedia Mahdy, Aid Daffa; Rusmana, Dadan
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.360

In recent years, paylater services in e-commerce, such as Shopee and Tokopedia, have experienced rapid growth by offering consumers the convenience of flexible shopping without paying in advance. This study aims to analyze the comparison of simple and compound interest models applied in the paylater schemes of the two platforms, with a focus on their impact on total payments and users' financial decisions. Primary data is obtained from the fee structure and interest rates applied by Shopee and Tokopedia, while secondary data is taken from related literature. The study used a quantitative approach with a case study method, involving payment simulations based on various nominal transaction scenarios (IDR 500,000, IDR 1,000,000, IDR 5,000,000) and tenors (3, 6, 12 months) calculated using Microsoft Excel. The results of the study show that Tokopedia offers a more economical fee structure, especially for large amounts and long tenors, with an interest of 2.75% per month and a fixed handling fee of IDR 5,000. In contrast, Shopee, although applying an interest of 2.95% per month, has a higher total payment due to a percentage-based handling fee of 1%. This study provides recommendations to users to choose a platform according to their financial needs. Tokopedia is recommended for large transactions and long tenors, while Shopee is more suitable for small transactions with short tenors. These results are also expected to be a reference for service providers to adjust their policies to improve competitiveness and user satisfaction. This study provides in-depth insight into the influence of interest models in paylater schemes and helps users make wiser financial decisions.

Collective Risk Model Analysis with Negative Binomial Distribution for Claim Amount and Discrete Uniform Distribution for Claim Amount Al Ayubi, Faqih Sholahuddin Al Ayubi
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.361

Insurance claims are requests submitted by policyholders to obtain protection against financial losses due to a risk. Individual claims arise whenever there is a risk, while aggregate claims are the number of individual claims in a certain period. These claims are important in managing insurance company expenses, especially for calculating aggregate losses, which are the total losses borne by the company. This study aims to analyze the average and variance of the number of claims distributed Negative Binomial and the amount of claims distributed Discrete Uniform using claim payment data from PT. Jasa Raharja (Persero) Representative of Purwakarta during 2018-2020. The collective risk model is used with the help of Easyfit software to determine the best distribution. The results of the analysis show the number of claims distributed Negative Binomial with an average of 13.2 claims and a variance of 7.6 claims, while the amount of claims distributed Discrete Uniform with an average of IDR2,108,950,000 and a variance of IDR567,850,000. The average aggregate claim is IDR 27,800,000,000 with a variance of IDR 6,270,000,000 during the period. The conclusion of this study confirms the effectiveness of the collective risk model in modeling aggregate claims for insurance data.

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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
ijgor.iorajournal@gmail.com

Phone
+6285841953112

Journal Mail Official
ijgor.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