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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 260 Documents
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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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Abstract

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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Abstract

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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Abstract

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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Abstract

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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Abstract

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)
From Feature Description to UML Architecture: A Novel Framework for Automated Reasoning and Multimodal Evaluation of Component and Deployment Diagram Nguyen, Van-Viet; Nguyen, Huu-Khanh; Nguyen, Kim-Son; Luong, Thi Minh-Hue; Bui, Anh-Tu; Vu, Duc-Quang; Nguyen, The-Vinh
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Abstract

Background: Unified Modeling Language (UML) is fundamental to software architecture, yet the automated generation of high-level diagrams remains underexplored. Specifically, Component and Deployment diagrams pose significant challenges due to their high abstraction and complex architectural dependencies, which are difficult to infer from natural language descriptions alone. Objective: This study aimed to develop and validate a novel, end-to-end framework to bridge the gap between natural language feature descriptions and executable UML architectural diagrams. The primary goal was to fully automate the pipeline, from requirement generation to robust, multimodal validation of the final visual outputs. Methods: A quantitative study was conducted using a three-stage automated pipeline. First, LLaMA 3.2-1B-Instruct generated diverse feature descriptions. Second, DeepSeek-R1-Distill-Qwen-32B performed advanced reasoning to synthesize executable PlantUML code for Component and Deployment diagrams. Finally, a novel multimodal validation framework was introduced, employing an ensemble of three vision-language models—Qwen2.5-VL-3B, LLaMA-3.2-11B-Vision, and Aya-Vision-8B—to quantitatively assess the fidelity of the generated diagrams against their source descriptions. Results: Our framework demonstrated high fidelity in accurately capturing both system modularity (Component diagrams) and runtime allocation (Deployment diagrams). The reasoning-driven synthesis by DeepSeek-R1 significantly outperformed baseline models in generating architecturally correct diagrams. The multimodal evaluation pipeline effectively reduced scoring bias by integrating diverse validation perspectives. A key outcome is the creation of a systematically generated benchmark dataset of architectural diagrams. Conclusion: This study successfully establishes the viability of an AI-driven pipeline for automated UML architecture generation and validation. It provides three key contributions: the first fully automated pipeline for this task, a novel multimodal validation method, and a public benchmark dataset. This work lays a foundation for practical, AI-powered software architecture modeling. Future work should extend this framework to encompass behavioral UML diagrams.
Repolink: A Repository Driven Technique for Reconstruct-ing Missing Links in Business Process Model Kristina , Kristina; Shiddiqi, Ary Mazharuddin; Siahaan, Daniel Oranova; Forca, Adrian
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Abstract

Background: The development of modern organization emphasizes the importance of accurate and comprehensive business process models (BPMs). BPMs serves to provide clear work standards for business actors. Business Process Model and Notation (BPMN) is widely used to model and analyse business processes. However, BPM models in practice often contain missing or inconsistent control-flow links, which reduce model correctness and limit effective analysis. Existing BPM retrieval approaches mainly focus on similarity measurement and provide limited support for explicit missing-link reconstruction. Objective: This study aims to propose a repository-driven approach to detect and reconstruct missing control-flow links in BPMN models while preserving computational efficiency and explainability. Methods: This study employs a quantitative experimental methodology on the use of an application called Repolink., a graph-based technique that transforms BPMN models into directed graphs and computes structural similarity values using Graph Edit Distance combined with semantic weighting. A query BPMN model is compared against a repository of reference BPMN models to identify structural inconsistencies. Missing links are detected using adjacency comparison supported by forward and reverse mappings. Results: The results show that Repolink can detect and reconstruct missing control-flow links in various BPMN structures, including branching and loop-related patterns. It is also able to significantly generate efficient retrieval with an overall time complexity of , where  is the number of nodes and  is the number of repository models. Compared to existing methods, Repolink provides higher explainability by explicitly reporting missing edges. Conclusion: Repolink effectively supports missing-link reconstruction in BPMN models through a repository-driven and explainable approaches. While the method focuses on structural analysis rather than full behavioural semantics, it offers a practical solution for BPMN conformance checking and model debugging.   Keywords: Information Retrieval, Diagram Similarity, Structural Semantic, Graph Edit Distance, Greedy Algorithm
Enhancing Social Media Adoption Among Food and Beverage Microenterprises: The Mediating Role of Perceived Initial Trust Hernando, Hendrick; Yasirandi, Rahmat; Mataruka, Leo Tarambiwa
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: The availability of technological factors largely influences social media adoption as a business support tool for microenterprises worldwide. However, recent studies reveal inconsistencies in the direct influence of technological factors on social media adoption. The proposed mediating variable is essential to address those inconsistencies. Objective: This study aims to identify the mediating role of perceived initial trust in the nexus between technological factors and social media adoption. Methods: Data was collected from 200 owners or staff of Indonesian food and beverage (F&B) microenterprises using a close-ended online questionnaire. Partial least squares equation modeling (PLS-SEM) was utilized for the data analysis process. Results: Findings revealed that cost-effectiveness significantly influences social media adoption, while relative advantage and perceived compatibility have no significant direct impact. Based on the result of PLS-SEM, this study identified a full mediation role of perceived initial trust, especially for relative advantage and perceived compatibility. The presence of potential advantages and compatibility shapes users' decisions to adopt social media through their trust in the early stages.   Conclusion: This study improves the understanding of how perceived initial trust connects technological factors to social media adoption. Furthermore, our study suggests practical implications for the government to create a community of practice involving F&B microenterprise owners. This suggestion may enhance knowledge and trust in the use of social media.    Keywords: Social Media Adoption, Perceived Initial Trust, Technological Factors, F&B Microenterprises
Unsupervised Anomaly Detection in Hospital Wastewater Effluent Using Convolutional Autoencoder Hibban, Daffa Maulana; Surendro, Kridanto
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Background: Hospital wastewater treatment plants (WWTPs) play a crucial role in maintaining environmental sustainability. However, conventional monitoring has difficulty identifying minor differences in effluent quality, leading to non-compliance. While machine learning is increasingly applied in water quality analysis, the specific application of deep representation learning in hospital effluent analysis, focusing on identifying anomalies within stable and low variation factors, is not much explored. Objective: This study aims to evaluate the effectiveness of a proposed Convolutional Autoencoder (Conv-AE) for anomaly detection in the effluent of hospital WWTP. To ensure the efficacy of the algorithm, it is compared with two popular statistical algorithms: Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM). Methods: Internet of Things (IoT) sensor data covering pH, temperature, Total Dissolved Solids (TDS), and ammonia gas parameters were collected from the effluent tank of a hospital WWTP. The Conv-AE model was designed to learn the latent nonlinear representations of normal effluent patterns. The model’s performance was evaluated using precision, recall, F1-score, accuracy, and inference time metrics. Results: The proposed Conv-AE model performed best in terms of detection, having the best values ​​for all three metrics, with a recall of 0.980, an F1 score of 0.960, and an accuracy of 0.980. This indicates a robust ability to identify subtle deviations that statistical baselines often miss. In terms of operational feasibility, while the Isolation Forest baseline exhibited the fastest inference time of 0.000014 seconds, the Conv-AE remained highly efficient for real-time applications with a inference time of 0.000348 seconds. Conclusion: In conclusion, the Conv-AE algorithm offers an optimal trade-off between high detection sensitivity and operational feasibility. By prioritizing the minimization of false negatives, this deep learning approach provides a more reliable solution for safety-critical hospital effluent monitoring compared to traditional statistical partitioning methods.   Keywords: Anomaly Detection, Hospital Wastewater Treatment Plant (WWTP) Effluent, Convolutional Autoencoder, Deep Learning

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