International Journal of Global Operations Research
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
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<![CDATA[Calculating Insurance Premiums for Stroke Patients Using the Multistate Markov Chain Method ]]>
<![CDATA[Aufhar, Ihkam Amalul]]>;
<![CDATA[Fasya, Emir Shiddiq]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.333
<![CDATA[Health insurance premium is one of the important elements in the insurance industry that needs to be calculated correctly so that insurance companies can minimize risks and losses. In this study, insurance premiums for stroke patients are calculated by utilizing the Markov Chain method. This method is used to model the movement of a patient's health condition over time, considering various conditions such as recovery, relapse, or death. Each condition is represented by a state in the Markov Chain model, and the transition between states is calculated based on patient history data and transition probabilities. Based on the modeling results, a more accurate premium estimation is obtained compared to conventional methods, as it is able to consider the dynamics of changing health conditions. This research provides important insights for the insurance industry in risk management as well as more optimal premium calculations for patients with chronic diseases such as stroke.]]>
<![CDATA[Bankruptcy Prediction Analysis of Life Insurance Companies Using Altman Z-Score dan Ohlson O-Score Methods ]]>
<![CDATA[Bayyinah, Ayyinah Nur]]>;
<![CDATA[Helena, Putri Zahra]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.335
<![CDATA[Life insurance is one of the non-bank industries that offers guarantees to overcome risks. In its implementation, life insurance companies need to maintain the survival of the company in the midst of increasing economic competition so that they don't face the threat of bankruptcy. Bankruptcy itself is a legal status of a certain entity or company that cannot pay its debts to creditors and the company's operations cannot be continued due to lack of funds. This study aims to compare the accuracy of the Altman Z-Score and Ohlson O-Score methods in predicting the bankruptcy of life insurance companies in Indonesia, such as PT Axa Financial Indonesia, PT Taspen Life, Asuransi Jiwa Bersama (AJB) Bumiputera 1912, PT Avrist Assurance, and PT Reliance Life Insurance Indonesia. The data used in this study is secondary data in the form of financial statements of life insurance companies taken from the official website of the relevant company. The results showed that the comparison between the two models revealed that the Altman method is better in predicting company bankruptcy. This is because the Altman method has a more detailed classification of conditions compared to the Ohlson method.]]>
<![CDATA[Bankruptcy Prediction Analysis of General Insurance Companies Using the Ohlson Model ]]>
<![CDATA[Maharani, Asthie Zaskia]]>;
<![CDATA[Bisyarah, Sania]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.336
<![CDATA[General insurance companies play an important role in maintaining economic stability by transferring financial risks from individuals and companies to insurance companies. However, insurance companies are not immune to the risk of bankruptcy that can arise due to factors such as inability to manage claims, premium fluctuations, and insufficient capital. Early detection of potential bankruptcy becomes very important to prevent greater losses. This study aims to analyze the prediction of bankruptcy in general insurance companies in Indonesia using the Ohlson Model. The Ohlson model is based on logistic regression, taking into account several financial variables such as leverage, profitability, and company size to estimate the probability of bankruptcy. The results of the study are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets. From the analysis, it was found that out of 13 general insurance companies listed on the Indonesia Stock Exchange (IDX), the Ohlson value for all companies is below 0.38, which indicates that the sampled companies still have fairly good financial stability. The research results are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets.]]>
<![CDATA[Analysis Automobile Insurance Fraud Claim Using Decision Tree and Random Forest Method ]]>
<![CDATA[Wicaksono, Ridwan Lazuardy Bimo]]>;
<![CDATA[Rohman, Aletta Divna Valensia]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.337
<![CDATA[Insurance fraud, particularly in the automobile sector, poses significant financial risks to insurance companies. This study aims to analyze fraudulent claims in automobile insurance using Decision Tree and Random Forest methods. A dataset consisting of 10,000 entries was utilized, containing variables such as vehicle type, claim amount, and claim status. The Decision Tree method was employed for its interpretability, while Random Forest was used for its superior accuracy. Results indicated that the Random Forest model outperformed the Decision Tree model, achieving an accuracy of 51.37% compared to 50.47%. This research highlights the effectiveness of machine learning techniques in detecting insurance fraud and provides insights for insurers to enhance their fraud detection systems.]]>
<![CDATA[Financial Distress Analysis of Companies Carrying Out Mass Layoffs Throughout 2023 using the Altman Z-Score Method, Springate Method, and Zmijewski Method ]]>
<![CDATA[Puspitasari, Laras Dwi]]>;
<![CDATA[Syifana, Hani]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.338
<![CDATA[This research aims to estimate the financial condition of four companies that will carry out mass layoffs throughout 2023 using three financial distress prediction methods, namely the Z-Score Altman method, the Springate method and the Zmijewski method. Secondary data was used sourced from the financial reports of each company from 2022 to 2023. The research results showed that financial performance was analyzed using the Z-Score Altman method, the Springate method and the Zmijewski method in four companies, namely PT Net Visi Media Tbk for the period 2022-2023 classified as having the potential to experience bankruptcy, PT GoTo Gojek Tokopedia Tbk for the 2022-2023 period is classified as having a high potential for bankruptcy, PT Bukalapak.com Tbk for the 2022-2023 period is classified as being in good health, and JD.com, Inc. (JD) for the 2022-2023 period is classified as having absolutely no potential for bankruptcy.]]>
<![CDATA[Performance Comparison of Ant Colony Optimization and Artificial Bee Colony in Solving the Capacitated Vehicle Routing Problem ]]>
<![CDATA[Setyawan, Deva Putra]]>;
<![CDATA[Lianingsih, Nestia]]>;
<![CDATA[Saputra, Moch Panji Agung]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.339
<![CDATA[The Capacitated Vehicle Routing Problem (CVRP) is a combinatorial optimization problem widely applied in logistics and supply chain management. It involves determining the optimal routes for a fleet of vehicles with limited capacity to serve a set of customers with specific demands while minimizing travel costs. This study compares the performance of two popular metaheuristic algorithms, Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), in solving the CVRP. The research implements both algorithms on standard benchmark datasets, evaluating solution accuracy and computational efficiency. Simulation results indicate that ACO tends to excel in finding high-quality solutions, particularly for problems with high complexity, whereas ABC demonstrates superior computational efficiency on small- to medium-scale datasets. A detailed analysis of algorithm parameters was also conducted to understand their impact on the performance of both methods. This study provides valuable insights into the strengths and limitations of each algorithm in the context of CVRP and paves the way for the development of hybrid approaches in the future.]]>
<![CDATA[Game Theory as a Marketing Optimization Tool: A Case Study on Kelom Geulis ]]>
<![CDATA[Azahra, Astrid Sulistya]]>;
<![CDATA[Saefullah, Rifki]]>;
<![CDATA[Wahid, Alim Jaizul]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.340
<![CDATA[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]]>
<![CDATA[Analysis of Queueing Systems in Fast Food Restaurants Using the M/M/c Model: A Case Study during Peak Hours ]]>
<![CDATA[Hidayana, Rizki Apriva]]>;
<![CDATA[Yohandoko, Setyo Luthfi Okta]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.341
<![CDATA[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.]]>
<![CDATA[Modeling of COVID-19 Growth Cases in Bandung Regency and Bandung City Using Vector Autoregression ]]>
<![CDATA[Megantara, Tubagus Robbi]]>;
<![CDATA[Hidayana, Rizki Apriva]]>;
<![CDATA[Syarifudin, Abdul Gazir]]>;
<![CDATA[Amelia, Rika]]>;
<![CDATA[Nurkholipah, Nenden Siti]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
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DOI: 10.47194/ijgor.v5i4.342
<![CDATA[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.]]>
<![CDATA[Sentiment Analysis of Tiktok App Reviews on Google Play using Several Machine Learning Methods ]]>
<![CDATA[Suhaimi, Nurnisaa binti Abdullah]]>;
<![CDATA[Lestari, Mugi]]>
<![CDATA[ International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v5i4.343
<![CDATA[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.]]>
