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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
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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Abstract

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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Abstract

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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