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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 14 Documents
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Search results for , issue "Vol. 12 No. 1 (2026): February" : 14 Documents clear
A Designing an Outsourcing Governance Framework for Strategic IS Management: A Systematic Literature Review Approach Elnakeep, Eman; Mazen, Sherif; Helal, Iman
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Outsourcing has become a critical strategy for organizations aiming to enhance efficiency, reduce costs, and maintain focus on core competencies. Objective: Effective governance remains a significant challenge, particularly in managing long-term and complex outsourcing relationships. Methods: This study proposes a comprehensive governance framework for outsourcing, developed through a systematic literature review (SLR) of models and frameworks published between 2021 and 2025. The proposed framework comprises six interrelated steps that integrate strategic alignment, delivery models, relational and contractual governance, performance monitoring, and innovation enablement. To assess the framework’s practical relevance, a qualitative survey was conducted among outsourcing professionals in Egypt. Results: Results show that over 90% of respondents rated the framework as either good or excellent, confirming its effectiveness and applicability. Conclusion: This study contributes a structured and adaptable governance model for improving the outcomes of outsourcing initiatives and offers a foundation for future empirical validation across sectors and geographies.   Keywords: Outsourcing, Governance Model, Practices, Relationships
Understanding Project Complexity Influences on Complex IT Project Success Rizky, Fajar; Raharjo, Teguh; Trisnawaty, Ni Wayan
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: The increasing complexity of IT projects in government organizations poses significant challenges for civil servants managing them. Previous studies suggest that complexity encompassing organizational, technological, and administrative dimensions can significantly affect IT project success. However, the specific impact of each complexity type on project innovation and success in the public sector, particularly in Indonesia, remains underexplored. Objective: The study investigates how project complexity affects the success of IT projects in Indonesia's public sector, focusing on civil servant's perspectives. The need arises from challenges in managing complex projects, particularly in organizational, technological, and administrative dimensions. Methods: This quantitative research employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data collected from 139 Indonesian civil servants. The survey used a Likert-scale questionnaire to measure the impact of project complexity on IT project success through the lens of project innovation. Results: The study revealed that organizational and technological complexities are crucial in fostering innovation within IT projects, ultimately enhancing their success. The findings indicate that when project teams effectively navigate organizational structures and adapt to technological challenges, they can cultivate an innovative environment that improves project outcomes. Administrative complexity does not significantly influence project innovation, indicating that rigid bureaucratic processes may fail to support creative problem-solving or achieve project objectives. Overall, the study underscores the importance of managing key aspects of project complexity to achieve higher success rates in complex IT projects within Indonesia’s public sector. Conclusion: The study emphasizes managing organizational and technological complexities to enhance innovation and project success in Indonesia’s public sector. The insignificant impact of administrative complexity suggests that rigid bureaucracy may hinder innovation. Future research should explore strategies to simplify administration and improve project management in government institutions.   Keywords: Project Complexity, IT Project Success, Civil Servants, Public Sector, PLS-SEM
An Enhanced Model for Evaluating Learning Satisfaction in Teaching User Stories: A Confirmatory Factor Analysis Approach Zul, Muhammad Ihsan; Yasin, Suhaila Mohd.; Sahid, Dadang Syarif Sihabudin
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Understanding how students experience and perceive learning through user stories provides valuable insights into the effectiveness of instructional design. Huang proposed a learning satisfaction framework in which students’ satisfaction emerges from four factors, namely perceived ease of use (PEOU), perceived usefulness (PU), learning motivation (PM), and overall learning satisfaction (LS). A recent study applied this model to teaching user stories in a software engineering course using Confirmatory Factor Analysis (CFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) but reported suboptimal model fit, with unsatisfactory SRMR and NFI values, indicating the need for refinement. Objective: This study aims to evaluates an enhanced learning satisfaction model for teaching user stories by identifying key influencing factors, examining their relationships, and assessing construct validity and model fit improvements. Methods: To improve the model, additional theoretical paths were introduced, and survey data were collected from 142 randomly selected software engineering students. The refined model was evaluated using CFA and PLS-SEM, with model fit improvements assessed through SRMR and NFI values. Results: The analysis revealed that PEOU and LM significantly influence learning satisfaction, while PU affects satisfaction indirectly through motivation. These interactions among PU, PEOU, and LM explain how ease of use and usefulness enhance motivation, which in turn increases satisfaction. Furthermore, the enhanced model showed an improved fit compared to the previous version, with SRMR values decreasing from 0.092 to 0.076 and NFI improving from 0.765 to 0.813, confirming better construct validity and overall model fit. Conclusion: The addition of new direct paths from PEOU and PU to LS increased the model’s R² and Q² values, indicating stronger construct validity and better overall fit. The refined structure provides a more accurate representation of how satisfaction is formed and offers a validated instrument for evaluating student learning experiences in teaching user stories within software engineering course.   Keywords: learning satisfaction, user story, confirmatory factor analysis, model fit evaluation, PLS-SEM, software engineering education.
Machine Learning-Enhanced Portfolio Optimization for Tailored Investment Strategies Across Diverse Risk Appetites Nguyen, Minh Duc
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Although researchers have increasingly explored the combination of machine learning based return forecasts with traditional portfolio construction, the discussion about how these predictive models reshape established methods is still developing. One prominent direction involves extending the classical mean variance approach so that it incorporates forward looking estimates, which is often referred to as the Mean Variance with Forecasting (MVF) framework. In parallel, approaches such as Risk Parity Portfolios (RPP) and Maximum Drawdown Portfolios (MDP) continue to gain attention because they represent different perspectives on risk management. However, despite this growing interest, there is still limited empirical evidence on how Support Vector Regression (SVR) and Random Forest (RF) forecasts affect performance within these three frameworks, and this gap is particularly evident in emerging markets. Objective: This study examines how SVR and RF one day ahead return forecasts influence the risk adjusted performance, drawdown control, and diversification outcomes of the MVF, RPP, and MDP frameworks when applied to stocks in the VN-100 index between 2017 and 2024. The choice of these frameworks is intentional, as each reflects a different level of investor tolerance for risk. MVF tends to appeal to investors who place greater weight on potential returns, RPP seeks a more even distribution of risk which suits investors with a moderate stance, and MDP focuses on limiting losses, making it more suitable for investors who are highly cautious about downside risk. Methods: Daily returns of VN 100 stocks were standardized and then used as inputs for the SVR and RF models. The models were tuned through a grid search on data from 2017 to 2021 and evaluated on the remaining period up to 2024. After generating the return forecasts, portfolios were constructed under the MVF, RPP, and MDP frameworks, and their performance was assessed using monthly excess returns, the information ratio, and total returns in comparison with the VN-100 index. Results: The forecasts generated by SVR showed greater reliability than those obtained from the RF model, and this contributed to stronger risk adjusted performance when applied within the MVF framework. The MDP strategy, which places emphasis on limiting drawdowns, delivered solid protection against large losses, whereas the RPP approach produced more moderate returns along with improved consistency. Conclusion: In the end, matching forecasting techniques and portfolio construction methods with an investor’s risk preferences and view of the market is crucial, since overall performance is shaped by the interaction between predictive inputs and allocation rules. Looking ahead, future studies could investigate a wider range of forecasting models, incorporate transaction costs more explicitly, and explore adaptive forms of optimization that are capable of responding to changing market conditions.   Keywords: Machine Learning, Maximum Drawdown, Mean-Variance, Portfolio Optimization, Random Forest, Risk Parity, Support Vector Regression, VN-100 Index
LightGCN Experiment for Government E-service Recommendation Gantulga, Sarnai; Ganbold, Amarsanaa
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Abstract

Background: As government e-services expand, the need to offer personalized services to each citizen is becoming increasingly important. However, government systems face limitations in utilizing user and service-specific features for model training, as training data is typically restricted to historical service usage records. This constraint poses a significant challenge in delivering practical, personalized recommendations. Objective: This study aims to demonstrate the feasibility of detecting latent collaborative filtering signals in government e-service usage data using a GNN-based approach, and to evaluate how effective graph neural network-based recommendation methods are at identifying these signals using only historical interaction records. Methods: Accordingly, we explore the application of LightGCN to model user-service interactions based solely on historical behavioral data. In this study, we constructed a bipartite graph from real-world usage data and trained a model to uncover latent patterns in user preferences. Results: Through hyperparameter tuning, our experiments achieved the following performance metrics: Recall@20 = 0.175, Precision@20 = 0.068, and NDCG@20 = 0.155. Conclusion: These results support our hypothesis, demonstrating that the graph neural network-based model can capture latent collaborative signals even under sparse data conditions. Consequently, LightGCN presents a promising approach for generating personalized recommendations in the context of government e-services.   Keywords: Collaborative Filtering, E-service, GNN-based Recommendation System, LightGCN, Recommendation System
Deep Learning Architecture with Attention-Enhanced U-Net for Analyzing Cell Nuclei in H&E-Stained Tissue Slides Hayat, Cynthia; Soenandi, Iwan Aang
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Accurate segmentation of cell nuclei in histopathological images plays a crucial role in computational pathology, as the results serve as a foundation for various clinical practices, including disease diagnosis, prediction, and prognosis. Deep learning methods like U-Net have greatly enhanced performance, but challenges such as tissue heterogeneity, cell nucleus overlap, and complex staining patterns still exist. Objective: This study aims to assess the effectiveness of the Attention Mechanism model within the U-Net architecture for cell nucleus segmentation in Hematoxylin and Eosin (H&E) stained histopathology images. By focusing on relevant spatial features, the Attention Mechanism is expected to improve the model’s ability to accurately distinguish and segment areas with overlapping cells. Specifically, this study also aims to examine whether the proposed model outperforms the conventional U-Net model. Methods: This study used a quantitative experimental approach, utilizing an H&E-stained histopathology image dataset from Saitama Medical University International Medical Center (SIMC). The Attention-Enhanced U-Net Model was trained and tested on pathologist-annotated cell nucleus data, then evaluated using performance metrics such as Dice Coefficient, Accuracy, Precision, Recall, F1-Score, AUROC Mean, and Intersection over Union (IoU). The experimental results showed that the model produced a Dice Coefficient of 0.927, Precision of 0.889, Recall of 0.861, F1-Score of 0.875, and IoU of 0.793. These findings indicate that the model can accurately capture the structure of a cell nucleus, even in challenging conditions such as different cell shapes and the presence of H&E staining. Results: Furthermore, integrating Attention Mechanisms allows the model to focus on extracting relevant features while reducing background noise. This improves its potential as a reliable segmentation solution in clinical pathology workflows. For future research, it is recommended to validate the model using a larger, more diverse dataset to improve its generalization and reliability in real-world clinical practice. Conclusion: The research concludes that the Attention-Enhanced U-Net model effectively achieves high-precision cell nucleus segmentation in H&E-stained histopathology images. It demonstrates strong performance across five metrics: Dice (0.927), Precision (0.889), Recall (0.861), F1-Score (0.875), and IoU (0.793). The model accurately detects nuclei, even in challenging conditions such as morphological variation, staining artifacts, and overlapping structures. Its attention mechanism improves feature extraction by focusing on relevant regions and reducing background noise, enhancing localization and delineation. The lightweight design supports clinical use with limited resources. Future studies should validate its generalizability on larger, more diverse datasets and clinical scenarios.   Keywords: Cell Nuclei Segmentation, Attention Enhanced U-Net, H&E Staining; Deep Learning, Medical Image Analysis.
Leveraging Geographic Information Systems for Polio Surveillance and Eradication: A Systematic Review Ahmed, Salmana; Imran, Mohammad; Ullah, Shafi; Azhar , Shanila; Mohammad Ashraf; Zahilah , Raja
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: The eradication of Polio virus is one of the important public health issues. In order to remove this virus, some well-planned methods are required. Objective: The aim of this paper is to assess GIS usage in different aspects of surveillance of polio virus. Specifically, the focus of this research is GIS applications, their practical challenges, and limitations in the context of polio viruses. Methods: Based on the systematic review of 36 peer reviewed studies across several academic research databases, we evaluated how GIS is used in polio control and eradication. Results: The findings include applications of GIS in several aspects. Few of these include vaccination coverage, analysis of disease patterns, and integration with existing healthcare information networks. Moreover, several important obstacles were also identified in context of shortcomings of data, infrastructure and expertise. Conclusion: The findings of this study offer useful guidance for healthcare workers and policymakers involved in eradication of polio viruses.   Keywords: Challenges, Geographic Information Systems (GIS), Polio virus, Surveillance, Systematic Review
Ripeness Detection and Shelf Life Prediction of Avocados Using YOLOv8 and Hybrid Machine Learning Ulfa, Maria; Abidin, Taufik Fuadi; Muchtar, Kahlil
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Post-harvest ripening for Hass avocados is hard because their ripening pattern is unpredictable. Recent studies have explored deep learning applications for ripening stages classification. However, it needs further development to achieve better results that can run effectively on low-end devices like smartphones. Objective: The study aims to achieve three main goals that involve developing an efficient YOLOv8 object detection model that works with local avocado varieties to precisely determine ripening stages, creating a hybrid machine learning model to improve classification performance, and predicting storage duration (shelf life). The study also aims to develop a practical mobile application for post-harvest use. Methods: The YOLOv8 model was trained first on the Hass avocado dataset for transfer learning, followed by a second training on the local avocado dataset for domain adaptation. Mean Average Precision (mAP) was used to evaluate a standalone YOLOv8 object detection model, while accuracy and F1-score were used to evaluate classification. The hybrid YOLOv8 and machine learning approach includes selecting the optimal YOLOv8 layer for feature extraction, using Random Forest for feature selection, applying SMOTE to handle data imbalance, and classifying with Logistic Regression, SVM, and XGBoost. Storage duration was performed using a standalone YOLOv8 and hybrid ML classification results, along with a linear regression formula. Results: The standalone YOLOv8 model achieved an mAP50 of 0.93 and a classification accuracy of 0.90 on the local avocado dataset. The hybrid method achieved a classification accuracy of 0.96. The storage estimation results showed that the hybrid approach produced an MAE of 0.43 days, while the standalone approach produced 0.44 days. The results were better than previous studies, achieving an error rate of 0.96 days. Conclusion: The research achieved its goal of developing an improved approach to identify avocados and determine their storage duration. The hybrid model functions as a working solution that improves post-harvest operations using Android-based applications.   Keywords: YOLOv8, Machine Learning, Avocado Ripening, Shelf-life Estimation
Improving SAM-Road Model for Occlusion Handling in Road Networks Extraction from Satellite Images with Gamma Correction and Modified A* Algorithm Fathul'ibad, Mohammad; Firmansyah, Maolana; Syakrani, Nurjannah; Fauzi, Cholid
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Occlusion in satellite imagery often leads to disconnected road networks, reducing the quality and reliability of geospatial data, which in turn hampers infrastructure and transportation planning. Although models like SAM-Road show promising results, they still struggle in handling occluded areas, especially at intersections and curved roads. Some existing methods, such as the extended-line approach, have been proposed to address occlusion; however, they are typically limited to handling linear road segments and are less effective in complex structures. To overcome these limitations, this study enhances the SAM-Road framework by incorporating gamma correction and a modified A algorithm in the post-processing stage. This combined approach improves the visibility of partially hidden roads and successfully reconnects fragmented segments, even in non-linear and occlusion-heavy areas. Objective: This study aims to improve the accuracy of road network extraction by enhancing the SAM-Road model with gamma correction and a modified A* algorithm, specifically to address the problem of occlusion in satellite imagery. Methods: This research adopts a quantitative experimental approach. Gamma correction is applied to enhance the visual contrast of roads in occluded satellite images, while the A pathfinding algorithm is modified to reconnect disjointed road segments. The integrated method is then evaluated using accuracy metrics, specifically the TOPO and APLS (Average Path Length Similarity) scores. Results: The experimental findings indicate that each method—SAM-Road baseline, gamma correction, the modified A* algorithm, and their combination—delivers distinct performance improvements. Gamma correction alone achieves the best results at gamma 1.5 (TOPO 80.61%) and 1.25 (APLS 70.91%) on SpaceNet, and gamma 2.0 (TOPO 78.56%) and 1.25 (APLS 68.73%) on City-scale. The modified A* algorithm performs best at 16/8 (TOPO 80.22%) and 32/16 (APLS 70.71%) on SpaceNet, and 16/8 (TOPO 77.29%) and 64/32 (APLS 70.94%) on City-scale. When combined, the method yields results within the range of TOPO 75.41–80.59% and APLS 67.59–71.17% on SpaceNet, and TOPO 76.52–78.56% and APLS 66.39–71.19% on City-scale. Conclusion: This study concludes that the integration of gamma correction and a modified A* algorithm effectively addresses occlusion-related challenges in satellite imagery. While each technique contributes unique improvements, their combination significantly enhances the accuracy and continuity of extracted road networks—not only in straight road segments such as extended-line method, but also in more complex or occluded areas. The results confirm that this hybrid approach yields road extraction outputs that more closely align with ground truth in terms of topology and structure. Future research could explore integrating gamma correction and the modified A* algorithm directly into the training process, aiming to enhance model performance while maintaining high accuracy.   Keywords: SAM-Road, modified A*, gamma correction, occlusion, road network extraction, satellite imagery, topology, geometry
An Empirical Study on Nurses' Acceptance of an IoT-Based Mobile Nursing Information System in the Operating Room Huan, Dou; Mohd Rafiz Salji; Mohd Zool Hilmie Bin Mohamed Sawal
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: The continuous progress of information technology has promoted the wide application of the Internet of Things (IoT)-based mobile nursing information system (MNIS) in the medical field. These systems are particularly valuable in high-intensity environments such as operating rooms, where efficiency, accuracy of records and patient safety are essential. However, there is still a lack of empirical evidence of the use of MNIS by nursing staff in such high-risk clinical environments. Objective: This study aims to explore the acceptance of MNIS by operating room nurses and identify the key factors that affect their intention to use the system. Methods: This study adopts quantitative research methods, guided by the technology acceptance model (TAM) and information system success model (ISSM) of DeLone and McLean. The data is collected through structured questionnaires, and the measurement content includes perceived usefulness (PU), perceived ease of use (PEOU), information quality (IQ), system quality (SQ), service quality (SEQ) and willingness to use. A total of 113 valid questionnaires were collected and analyzed by statistical methods. Results: The research results show that perceived usefulness (PU), perceived ease of use (PEOU) and service quality (SEQ) have a significant positive impact on nurses' acceptance of mobile nursing information system (MNIS) (p < 0.01). Information quality (IQ) and system quality (SQ) also show moderate but less significant effects. These results support the applicability of the Technology Acceptance Model (TAM) and the Information System Service Model (ISSM) in explaining the technology acceptance in the operating room environment. Conclusion: The study confirms that perceived usefulness (PU), perceived ease of use (PEOU) and service quality (SEQ) are crucial to promote the adoption of mobile nursing information system (MNIS) for operating room nurses. These findings provide valuable guidance for hospital administrators and system developers who aim to strengthen the digital integration of clinical care. Future research should explore the long-term satisfaction, cost-effectiveness, and integration of MNIS across departments and hospital systems.   Keywords: Internet of Things (IoT), Mobile Nursing Information System (MNIS), Operating Room, Nurse Acceptance, Technology Acceptance Model (TAM), Information Systems Success Model (ISSM)

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