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
Articles
164 Documents
<![CDATA[The Application of Dynamic Simulation for Determining Competitive Sales Strategies of Cassava Chips Using iThink ]]>
<![CDATA[Falleryan, Muhammad]]>;
<![CDATA[Juanito, Axel]]>;
<![CDATA[Nurcahya, Dimas]]>;
<![CDATA[Tosida, Eneng Tita]]>;
<![CDATA[Subandi, Kotim]]>;
<![CDATA[Sugara, Victor Ilyas]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i2.377
<![CDATA[This study aims to determine sales strategies for cassava chips through a dynamic simulation approach using iThink software. The simulation is used to model the factors influencing sales performance, such as fluctuations in raw material prices, operational costs, and market demand patterns. By employing a CLD (Causal Loop Diagram) model and dynamic simulation, this research evaluates various strategies, including product diversification, digital promotion, and distribution efficiency. The simulation results indicate that implementing strategies such as flavor variant diversification and increased promotion through social media can significantly improve sales and profits. Validation was carried out through sensitivity testing on cost and sales parameter changes, demonstrating that dynamic simulation can be an effective tool to support data-driven strategic decision-making]]>
<![CDATA[Forecasting the Unseen: A Stationary Distribution Approach to Earthquake Magnitude Prediction in Bengkulu ]]>
<![CDATA[Megantara, Tubagus Robbi]]>;
<![CDATA[Hidayana, Rizki Apriva]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i2.379
<![CDATA[Long-term forecasting of earthquake magnitudes plays a vital role in seismic hazard assessment and disaster mitigation, particularly in highly active seismic regions such as Bengkulu, Indonesia. This study introduces a probabilistic framework based on the stationary distribution of discrete-time Markov chains to predict the likelihood of various earthquake magnitudes over an extended period. Historical earthquake records from Bengkulu are categorized into discrete magnitude classes to form the states of the Markov chain. Transition probabilities between these states are estimated from the data, allowing for the construction of a transition matrix that accurately reflects the temporal dynamics of seismic activity. By analyzing the stationary distribution of this Markov chain, we derive the long-term probabilities of occurrence for each magnitude class, revealing inherent patterns in earthquake magnitudes that are otherwise difficult to capture with traditional methods. The stationary distribution serves as a stable, time-independent descriptor of the seismic regime, providing insights into the expected distribution of earthquake magnitudes in the future. The results indicate that this approach not only captures the probabilistic behaviour of seismic magnitudes but also offers a computationally efficient and interpretable model for earthquake forecasting. This modelling technique complements existing seismic hazard assessments and has practical implications for risk management and emergency preparedness in Bengkulu and other seismically active areas. Future research will explore the integration of spatial factors and earthquake depth to further enhance prediction accuracy.]]>
<![CDATA[Investment Strategy in the Banking Sector: Probability Ratio Analysis and Comparison of Financial Performance in Core Bank Group 4 (BCA, BRI, BNI, Mandiri) ]]>
<![CDATA[Wardoyo, Chess Satriya]]>;
<![CDATA[Suryadi, Joanes Ferdienand]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i2.380
<![CDATA[This study aims to analyze performance bank finances in KBMI group 4 (Bank BCA, BRI, BNI, and Mandiri ) with a focus on the ratio profitability as a foundation taking decision investment . Using the method descriptive comparatively , this study analyzes financial data in five year period Lastly , with a focus on the Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), and Cost of Goods (Cash) indicators . Operational to Income Operational (BOPO). Research results show that there is difference significant in profile profitability the four banks, with BCA showing consistency highest in profitability and efficiency operational . BRI shows superiority in NIM, while Mandiri and BNI show improvement stable performance . Investment strategy model formulated based on analysis risk -return, cycle economy , and trends industry banking . This research provides different investment strategy recommendations based on profile investor risk , taking into account special to impact digital transformation and change regulation in the sector Indonesian banking .]]>
<![CDATA[Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction ]]>
<![CDATA[Yuningsih, Siti Hadiaty]]>;
<![CDATA[Ismail, Muhammad Iqbal Al-Banna]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i2.381
<![CDATA[The rapid advancement of information technology and digitalization has significantly transformed the financial sector, particularly with the emergence of cryptocurrencies characterized by high price volatility and complex movement patterns. Accurate price prediction of these crypto assets is essential to support investment decision-making and risk management. This study aims to compare the performance of six activation functions ReLU, Tanh, Sigmoid, Softplus, Swish, and Mish in a Simple Recurrent Neural Network (RNN) model for predicting the price of Litecoin, a widely traded cryptocurrency. Using historical daily closing price data from May 2020 to April 2025, the data were preprocessed through Min-Max normalization and sliding window sequence formation to fit the RNN input requirements. Each activation function was applied in the RNN model under consistent training conditions, and model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Results indicate that the Swish activation function outperforms others by achieving the lowest RMSE of 4.58 and the highest R² score of 0.9578, demonstrating superior prediction accuracy and stable convergence. Tanh also showed competitive results, while Sigmoid and Softplus performed less effectively. In conclusion, Swish is recommended as the most suitable activation function for RNN-based cryptocurrency price forecasting due to its balance of accuracy and computational efficiency.]]>
<![CDATA[Application of Queue Theory in Campus Transportation at Padjadjaran Jatinangor University Using a Multiserver Queue System Model ]]>
<![CDATA[Aufhar, Ihkam Amalul]]>;
<![CDATA[Sudradjat]]>;
<![CDATA[Nahar, Julita]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.386
<![CDATA[Trasnportation system on campus is an important aspect that supports the mobility of the Academic Community and the relations or partners of Padjadjaran University. Currently, Padjadjaran Jatinangor University provides several public transportation facilities that can be used around campus area, namely conventional motorcycle taxis, Beam electric bicycles, and campus transportation in the form of buses. Based on the results of a survey conducted by the author, campus transportation is a facility of public transportation that is more often used and in demand by the Academic Community compared to the other two facilities of public transportation. This study aims to analyze the performance of the passenger queuing system on that campus transportation using a multiserver queue system model. Data in the arrival rate of bus passenger ( ) and the rate of bus service ( ) were collected through direct observation. The results of the study showed that during the operating hours at 07:00-08:00, routes A, B, and C are optimal with number of buses as many as 5, 5, and 6 respectively. Then, during operating hours at 09:45-10:45, routes B and C are optimal with number of buses each as many as 3 buses. As for route A, it is necessary to reduce the number of buses by 1 piece. Then during operating hours at 13:00-14:00, all routes need to be reduced to 1 bus each.]]>
<![CDATA[The Evolution of Financial Fraud Detection Methods: A Systematic Review of Integration of Theory, Data Analytics, and Artificial Intelligence ]]>
<![CDATA[Zaputra, Ali Rahman Reza]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.388
<![CDATA[Financial fraud is a persistent global threat that undermines the reliability of financial reporting, corporate governance, and economic stability. In Indonesia, recent high-profile cases such as the LPEI corruption scandal illustrate the limitations of existing fraud detection systems in identifying complex and concealed fraudulent behavior. The growing sophistication of fraud patterns, coupled with increased data volume and the digitization of financial systems, presents a significant challenge to traditional, manual-based detection methods. This highlights a critical gap in both theory and practice regarding how fraud is detected, interpreted, and prevented. This study aims to analyze and describe the evolution of financial fraud detection methods over the past decade and examine the role of Machine Learning (ML) and Explainable Artificial Intelligence (XAI) in enhancing accuracy and trust in financial fraud detection systems. A systematic literature review was conducted using the PICO framework, focusing on peer-reviewed articles published between 2019 and 2024 sourced from the Emerald Insight database. The results show a clear transition from traditional fraud detection approaches such as document analysis, field investigations, and interviews toward automated, data-driven techniques. The integration of ML algorithms, including Support Vector Machines, Random Forests, and unsupervised clustering, has improved fraud identification accuracy. Additionally, the use of XAI enhances model interpretability and stakeholder confidence by addressing the black-box nature of AI models. These technologies not only streamline detection processes but also reduce false positives and improve decision-making transparency. This research contributes to the literature by mapping the convergence of behavioral fraud theories and data science approaches. It also offers practical insights for organizations and auditors in developing adaptive, technology-integrated fraud detection frameworks that are both accurate and explainable.]]>
<![CDATA[Analysis of Health Insurance Claims Factors using The Stochastic Restricted Maximum Likelihood Estimation (SRMLE) Binary Logistic Regression Model: (Case Study: Health Insurance Claims at XYZ Company in 2023) ]]>
<![CDATA[Bagariang, Elizabeth Irene]]>;
<![CDATA[Riaman]]>;
<![CDATA[Gusriani, Nurul]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.389
<![CDATA[The health insurance claim approval process is a crucial aspect for insurance companies. Inaccuracy in predicting claim status can pose financial risks to the company and reduce policyholder trust. This study aims to identify the factors that influence the approval or rejection of health insurance claims. In this type of data analysis, the problem of multicollinearity among predictor variables is often encountered, which can lead to unstable parameter estimates. To address this issue, this study utilizes a binary logistic regression model with the Stochastic Restricted Maximum Likelihood Estimation (SRMLE) method, which is better suited to handle such conditions. The data used in this research includes the variables of total claim amount, premium price, number of insured individuals, employee age, and the number of previous claims recorded at XYZ Company. The results of the factor analysis, through the developed logistic regression model, show that the variables of total claim amount, premium price, and the number of insured individuals are significant factors influencing the probability of claim approval.]]>
<![CDATA[Analysis of Shoreline Changes Using the One-Line Model at Batu Karas Beach, Pangandaran, Indonesia ]]>
<![CDATA[Subiyanto]]>;
<![CDATA[Saqina Ramadhanti, Defania]]>;
<![CDATA[Abdurrahman, Umar]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.390
<![CDATA[This study aims to analyze shoreline changes numerically in the Batu Karas Beach area, Pangandaran, using the one-line model approach. This model is used to predict shoreline shifts resulting from abrasion and sedimentation processes influenced by wave and current dynamics. The simulation method employed is the finite difference method, which simplifies the problem of shoreline movement into a dominant one-dimensional form. Simulation results show that in several segments of the beach, abrasion occurs at an average rate of 35.94 meters per year, while in other segments, accretion occurs at a rate of 34.32 meters per year. These findings provide important insights into coastal dynamics that can be used to support sustainable coastal management and protection planning.]]>
<![CDATA[Growth Response of Sea Kale (Ipomoea Pes-Caprae (L.) R. Br.) to Porong River Sediment Polluted by Lapindo Mud ]]>
<![CDATA[Budiono, Ruly]]>;
<![CDATA[Kusmoro, Joko]]>;
<![CDATA[Dwiputri, Novia Amanda]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.391
<![CDATA[Vegetative growth of sea kale (Ipomoea pes-caprae) can be affected by the condition of the growing medium, including sediments polluted by Lapindo mud. This study aimed to explore the effect of polluted sediments on the growth of stem cuttings of I. pes-caprae in the Porong River area. The study used an experimental approach with a completely randomized design (CRD), testing six growing media treatments: sediments of the Porong River not polluted by Lapindo mud (positive control), pure Lapindo mud (negative control), and sediments from four different locations located 0 km, 7 km, 14 km, and 21 km from the mud discharge point. Observation parameters included survival, number of shoots, shoot length, number of leaves, leaf size, root length, biomass, biomass efficiency, and root to crown ratio. Data were analyzed using one-way ANOVA with Tukey's further test at 95% confidence level. Results showed that 0 km media supported the highest growth consistently, while 7 km and 21 km media showed fluctuating results. These findings suggest that I. pes-caprae has tolerance to sediments with mild to moderate contamination, and has the potential to be used as a pioneer plant in passive revegetation and phytoremediation programs in the Porong River area affected by Lapindo mudflow.]]>
<![CDATA[Comparative Analysis of Machine Learning Models for Email Spam Detection ]]>
<![CDATA[Lestari, Mugi]]>;
<![CDATA[Salih, Yasir]]>;
<![CDATA[Jaizul, Alim]]>
<![CDATA[ International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025 ]]>
Publisher : <![CDATA[iora ]]>
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DOI: 10.47194/ijgor.v6i3.392
<![CDATA[The development of information technology has driven a significant increase in the use of email as a primary communication tool across various sectors. Spam emails have become a serious issue that can disrupt productivity and threaten data security as well as user privacy. Conventional rule-based spam filtering systems are no longer considered effective in countering increasingly sophisticated and adaptive spam attack patterns. A more dynamic and accurate approach is required through the utilization of Machine Learning. This study aims to analyze and compare the performance of several Machine Learning algorithms in detecting spam emails, namely Extra Trees Classifier, Random Forest, Support Vector Machine (SVM) with an RBF kernel, and CatBoost. The methodology involves data acquisition from the SMS Spam Collection Dataset, data preprocessing through text cleaning and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), followed by model training and evaluation using Accuracy, F1 Score, and ROC AUC metrics. The results show that the Extra Trees Classifier achieved the best performance, with an Accuracy of 97.29%, an F1 Score of 0.8814, and a ROC AUC of 0.9868. Tree-based ensemble models, particularly Extra Trees and Random Forest, demonstrated superior capability in maintaining a balance between precision and recall. The SVM (RBF) recorded the highest AUC value but presented a trade-off in the form of a higher number of False Negatives. The findings of this research serve as a reference for the development of more adaptive and effective Machine Learning–based spam detection systems.]]>
