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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 174 Documents

14 15 16 17 18

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.

Bankruptcy Prediction and Financial Risk Analysis of PT AIA Financial and PT Allianz Life Insurance Indonesia Using The Altman Z-Score Model Fitrasia, Muthia; Pasha, Raisa
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.405

This study analyzes and compares the bankruptcy risk of PT AIA Financial and PT Asuransi Allianz Life Indonesia using the Altman Z-Score model based on financial reports for the period 2018–2024. The results show that both companies are in the grey zone, indicating potential financial risk even though they have not actually gone bankrupt. PT Allianz was in the distress zone in 2018 before improving, while PT AIA showed better stability with an upward trend in Z-Score in recent years. The Altman Z-Score model has proven to be effective as an early warning system for monitoring the financial health of insurance companies and supporting decision-making in risk mitigation.

Application of the Geometric Brownian Motion Model and Value at Risk Calculation on the Stock of PT Bank Tabungan Negara (Persero) Tbk: Stock of PT Bank Tabungan Negara (Persero) Tbk Putri, Natasya Pradini; Palbeno, Angela Ratna Sari
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.407

The fluctuating nature of stock prices creates risks for investors, making quantitative methods essential for predicting price movements and estimating potential losses. This study applies the Geometric Brownian Motion (GBM) model to simulate the stock price dynamics of PT Bank Tabungan Negara (Persero) Tbk (BBTN) and calculates the Value at Risk (VaR) using the Monte Carlo simulation method. Daily closing price data from May 26 to September 26, 2025, were analyzed and confirmed to follow a normal distribution based on the Kolmogorov–Smirnov test. The results indicate a high prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 7.95%. The estimated daily VaR for an initial capital of IDR 100,000,000 ranges from IDR 97,974 to IDR 114,045, corresponding to confidence levels between 80% and 99%. Keywords: Geometric Brownian Motion, Value at Risk, Monte Carlo, stock.

Sensitivity of Premium Calculations to Distribution Assumptions: The Impact of Using Pareto vs. Pareto Distributions Exponential on Claims of Large Losses hadiat, aisha; Anjani, Niken Pinkyvita Nur
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.409

Premium calculation is a fundamental aspect of the insurance industry to ensure the sustainability and profitability of companies. One of the main factors that affect the accuracy of premium calculation is the selection of claim probability distribution, especially for large claims that have the potential to cause significant losses. Exponential distribution is often used because of its simplicity, but its light tail characteristic makes it less suitable for modeling extreme claims. Conversely, the Pareto distribution, with its heavy-tailed nature, is considered more representative in capturing the risk of large claims. This study aims to analyze the sensitivity of premium calculations to distribution assumptions by comparing the use of exponential and Pareto distributions on large loss claim data. This analysis is expected to provide a practical overview of the impact of distribution selection on premium setting and its implications for insurance risk management in Indonesia.

An Actuarial Approach to Determining the Optimal Premium for Car Accident Insurance Based on Claim Frequency and Severity Data Kayla, Ailany; Fidela, Riesta
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.412

The determination of car accident insurance premiums requires a model capable of capturing variations in claim frequency and claim severity to ensure fairness and competitiveness. This study employs aggregate claim distributions—Negative Binomial–Exponential and Negative Binomial–Gamma—under two approaches: the Pure Premium Principle and the Expected Value Principle. the analysis indicates that the Expected Value Principle is more optimal, as it incorporates a premium loading factor (ψ) to account for additional risk adjustments. The calculations yield premiums of IDR 2,802,908,472.03 for the Negative Binomial–Exponential model and IDR 5,566,024,615.90 for the Negative Binomial–Gamma model. The Negative Binomial–Gamma model was selected as the optimal premium calculation model, resulting in a final premium of IDR 4,619,107.565 after dividing by the total claim frequency of 1,205. These findings confirm that the choice of aggregate claim distribution significantly affects the premium amount and provides a stronger foundation for insurance companies to establish sound and competitive pricing.

Bankruptcy Prediction Analysis of PT Bukalapak.com Tbk Using Altman Z-Score and Springate S-Score Models for the 2022-2024 Period Barri, Safiq Rofiul; Wardoyo, Catur Satriya
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.423

The rapid development of the digital technology industry in Indonesia has made e-commerce companies like PT Bukalapak Tbk face complex financial dynamics, including the potential risk of bankruptcy. This study aims to analyze and compare the potential bankruptcy of PT Bukalapak Tbk in the 2022–2024 period using two prediction models: the Altman Z-Score and the Springate S-Score. This study uses a quantitative approach with a descriptive-comparative method. The data used are secondary data in the form of PT Bukalapak Tbk's annual financial reports published on the Indonesia Stock Exchange and the company's official website. The analysis was conducted by calculating key financial ratios, which were then entered into the Altman and Springate formulas to obtain prediction scores. The calculation results show differences in trends between the two models. The Altman Z-Score model indicates that the company is in the gray zone to distress, indicating a potential risk of bankruptcy if financial structure improvements are not made. Meanwhile, the Springate S-Score model provides relatively more optimistic results with a tendency to be above the healthy threshold, although it still shows fluctuations in financial performance during the study period. These findings indicate that the sensitivity and focus of the variables used by each model influence the differences in prediction results. This research confirms that applying bankruptcy prediction models to digital technology companies requires considering the characteristics of industries with high operational costs but significant growth potential. The research findings are expected to contribute to company management, investors, and stakeholders in making strategic decisions based on financial risk analysis. Keywords: Bankruptcy, Altman Z-Score, Springate S-Score, Financial Analysis, PT Bukalapak Tbk

Comparison of Machine Learning Models for Breast Cancer Diagnosis Classification Ibrahim, Riza; Yuningsih, Siti Hadiaty; Ismail, Muhammad Iqbal Al-Banna
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.431

Breast cancer remains one of the most pressing global public health challenges, with approximately 2.3 million women diagnosed worldwide in 2022 and around 670,000 deaths attributed to the disease. Despite the widespread application of machine learning algorithms for breast cancer classification, findings across studies remain highly varied, and there is still no consistent conclusion regarding which algorithm is most superior for breast cancer diagnosis. This study aims to analyze and compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) in predicting breast cancer. The dataset used was the Breast Cancer Wisconsin (Diagnostic) Data Set obtained from Kaggle, containing morphological characteristics of tumor cells. Data preprocessing involved cleaning, label encoding, feature normalization using StandardScaler, and an 80:20 train-test split. Model performance was evaluated using confusion matrix, precision, recall, F1-score, accuracy, and ROC-AUC. The results showed that all four models achieved excellent performance with overall accuracy ranging from 95.61% to 97.37%. SVM emerged as the most accurate model (97.37%) with perfect recall (1.00) for the Benign class. Logistic Regression demonstrated the highest ROC-AUC value (0.9960), indicating excellent discriminative ability. Random Forest and KNN showed slightly lower performance, particularly in detecting Malignant cases with recall of 0.90. These findings confirm that machine learning can serve as an effective tool to support breast cancer diagnosis, with algorithm selection depending on data characteristics and clinical priorities.

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Editorial Address Jl. Merkuri Timur VI No. 1, RT. 007, RW. 004, Manjahlega, Rancasari, 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