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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 10 Documents

Search results for , issue "Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025" : 10 Documents clear

Application of Queue Theory in Campus Transportation at Padjadjaran Jatinangor University Using a Multiserver Queue System Model Aufhar, Ihkam Amalul; Sudradjat; Nahar, Julita
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.386

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.

The Evolution of Financial Fraud Detection Methods: A Systematic Review of Integration of Theory, Data Analytics, and Artificial Intelligence Zaputra, Ali Rahman Reza
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.388

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.

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) Bagariang, Elizabeth Irene; Riaman; Gusriani, Nurul
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.389

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.

Analysis of Shoreline Changes Using the One-Line Model at Batu Karas Beach, Pangandaran, Indonesia Subiyanto; Saqina Ramadhanti, Defania; Abdurrahman, Umar
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.390

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.

Growth Response of Sea Kale (Ipomoea Pes-Caprae (L.) R. Br.) to Porong River Sediment Polluted by Lapindo Mud Budiono, Ruly; Kusmoro, Joko; Dwiputri, Novia Amanda
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.391

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.

Comparative Analysis of Machine Learning Models for Email Spam Detection Lestari, Mugi; Salih, Yasir; Jaizul, Alim
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.392

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.

Information Quality and Compatibility as Determinants of M-Wallet Usage in Indonesia. Prakarsa, Graha; Nursyanti, Reni; Putra, Prayuda Mulyadi; Saputra, Renda Sandi
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.393

This study aims to assess the acceptance of mobile wallet applications in Indonesia by incorporating Information Quality and Compatibility as external factors within the framework of the Technology Acceptance Model (TAM). A quantitative approach was employed, and data from 208 respondents were analyzed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The findings indicate that both Information Quality and Compatibility have a positive and significant influence on Perceived Usefulness and Perceived Ease of Use. Furthermore, these two variables also significantly affect Continuance Intention to Use, which subsequently impacts the Actual Use of mobile wallets. Overall, Information Quality and Compatibility contribute 56% to Perceived Usefulness, 52.4% to Perceived Ease of Use, and 43.8% to Continuance Intention to Use. These findings offer valuable insights for application developers seeking to enhance mobile wallet adoption in Indonesia.

Stock Portfolio Optimization of IDX30 using Agglomerative Hierarchical Clustering and Ant Colony Optimization Algorithm Firdaus, Muhammad Rayhan; Subartini, Betty; Sukono, Sukono
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.394

The stock market offers high profit opportunities but also entails significant risks, making portfolio optimization essential to help investors manage risk and maximize returns. This study aims to cluster IDX30 stocks to form a more diversified portfolio, determine the optimal stock weights, and evaluate portfolio performance. The method employed is Agglomerative Hierarchical Clustering (AHC) with Ward linkage for clustering stocks based on financial ratios, with the silhouette score used to evaluate cluster quality. Subsequently, the Ant Colony Optimization (ACO) algorithm is applied to optimize stock weights in the portfolio based on the clustering results. The findings indicate that the best portfolio is obtained in clusters 5 and 6, with a maximum fitness value of 0.064555 and a portfolio return of 0.000814. Portfolio performance evaluation using the Sharpe ratio yields a value of 0.044767 for both clusters, indicating that the resulting portfolios are efficient. This research is expected to contribute to the development of more accurate and practical data-driven investment strategies for investors.

Forecasting Rice Sales Using Weighted Moving Average Method: Case Study at KAKANG MART GROSIR Bandung Nurkholipah, Nenden Siti; Megantara, Tubagus Robbi; Hidayana, Rizki Apriva
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.395

Effective inventory management is critical for retail businesses, and accurate sales forecasting is its cornerstone, especially for staple products like rice. This study aims to forecast the sales of packaged rice at KAKANG MART GROSIR, a major retailer in Bandung, by analyzing its daily sales data. The research utilizes the Weighted Moving Average (WMA) method on primary sales data for six top-selling rice brands collected over a three-month period from March 1 to May 31, 2025. The WMA model, which assigns greater importance to recent observations, was employed to smooth short-term fluctuations and identify underlying sales trends. The analysis revealed highly dynamic and distinct sales patterns: the JM Cianjur brand showed the highest average sales but with significant weekly volatility , the Setrawangi RS brand demonstrated strong and consistent growth to become a market leader , while the Setrawangi DI brand experienced a sharp decline. Furthermore, the BMW brand was found to have remarkably stable and predictable sales , whereas the Lahap and Sedap Wangi brands consistently remained at the lowest sales tier. The findings confirm that the WMA is a valuable tool for identifying diverse sales trajectories, providing actionable insights for developing tailored inventory strategies for each product.

Comparative Analysis of Activation Functions in LSTM Models for Predicting Bank BNI Stock Prices Azahra, Astrid Sulistya; Saputra, Moch Panji Agung; Hidayana, Rizki Apriva
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.396

The Indonesian capital market has experienced rapid development in the last two decades, with the banking sector as one of the main drivers. Stock price prediction is a crucial aspect for investors and market players to minimize risk and optimize investment strategies. Price fluctuations influenced by fundamental factors, market sentiment, and external conditions make prediction a complex challenge. This study aims to compare the performance of four activation functions: Rectified Linear Unit (ReLU), hyperbolic tangent (Tanh), Sigmoid, and Softplus, in the Long Short-Term Memory (LSTM) model in predicting the stock price of Bank Negara Indonesia (BNI). The method used is a quantitative approach with experiments, using historical data of BNI's closing stock prices for the period May 1, 2020, to April 30, 2025, obtained from Yahoo Finance. The data is processed through cleaning, normalization, transformation into a supervised learning format, and division into training data (80%) and test data (20%). Performance evaluation is carried out using RMSE, MAE, MAPE, and R² metrics. The results showed that the Softplus activation function produced the best performance with RMSE 128.714, MAE 101.815, MAPE 2.358%, and R² 0.924, followed by ReLU which had competitive performance and more efficient training time. The Tanh activation function was in the middle position, while Sigmoid showed the lowest performance. These findings indicate that Softplus and ReLU are optimal choices for BNI stock price prediction using LSTM, with Softplus excelling in accuracy and ReLU providing a balance between performance and efficiency.

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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