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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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Diabetic Retinopathy Fundus Image Classification Using Self-Organizing Maps Prabowo, Yulius Denny; Dwiandiyanta, B. Yudi; Maslim, Martinus; Corradini, Andrea
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.500-513

Abstract

Background: Diabetic retinopathy (DR) is a condition that impairs the blood vessels in the retina, resulting in vision loss ranging from temporary to permanent blindness. It commonly affects individuals diagnosed with diabetes mellitus (DM). Fundoscopy is a technique used to identify DR by examining the fundus of the eye during an eye examination. This process is time-consuming and can be expensive. Objective: This study aimed to examine the identification of DR using digital image processing methods. Methods: The self-organizing map (SOM) artificial neural network was employed. Diabetic retinopathy will be categorized according to its severity, including normal, mild, moderate, or severe. This classification considers the quantity of exudates and microaneurysms and the blood vessel structure in the fundus image. The dataset used in this investigation comprised 1000 fundus images acquired from the MESSIDOR ophthalmology database. Results: The findings indicate that the SOM approach achieves a training accuracy of 72% and a testing accuracy of 62%. Conclusion: The DR severity classification system can effectively extract DR-related features by segmenting exudates, blood vessels, and microaneurysms from funduscopic images during training, testing, and evaluation. Keywords: Diabetic Retinopathy, Self-Organizing Map, Fundus Image Classification, Digital Image Processing
Exploring Service Quality and Consumer Acceptance of Autonomous Convenience Stores Goh, Chin Fei; Hii, Puong Koh; Mah, Ri Wei; Tan, Owee Kowang; Li, Wushuang
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.488-499

Abstract

Background: Automation is revolutionizing retail operations, leading consumers to increasingly interact with advanced retail technologies. While there have been studies on the influence of service quality on consumer acceptance, research examining the service quality of hybrid services and consumer acceptance in automated retail is limited.  Objective: This study aims to examine consumer acceptance of automated retail stores.   Methods: This study tested a proposed model by surveying 101 consumers and using a questionnaire for hypothesis testing. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the effect of e-service quality dimensions on technology acceptance (perceived ease of use, perceived usefulness, and behavior intention) in the context of unmanned automated retail stores.   Results: The findings reveal that information quality positively affects perceived ease of use, while system quality positively affects perceived usefulness.  Conclusion: This study generates new insights by incorporating e-service quality dimensions from the E-Service Quality model into the Technology Acceptance Model. Additionally, the results highlight the growing importance of seamless digital experiences and reliable systems in shaping user perceptions and behavioral intentions. These findings offer practical implications for retailers aiming to enhance customer satisfaction and adoption of unmanned retail technologies through improved service design and digital infrastructure. Future research can further explore other influencing factors such as trust, perceived risk, and user demographics to better understand the evolving dynamics of consumer-technology interaction in automated retail environments.    Keywords: artificial intelligence; autonomous convenience store; consumer acceptance; e-service quality; technology acceptance model 
Clustering and Mixture Modeling of Schooling Expectancy Trends in Papua Province: A Spatial Analysis Using the Mapping Toolbox Wororomi, Jonathan; Reba, Felix; Asmuruf, Frans
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.459-472

Abstract

Background: Persistent educational inequality in Papua Province, particularly in remote highland districts, is driven by limited infrastructure and accessibility. Although Schooling Expectancy (Harapan Lama Sekolah, HLS) is widely recognized as a forward-looking educational metric, existing studies rarely incorporate probabilistic modeling with spatial analysis to examine regional disparities. ObjectiveThis study aimed to identify spatial and statistical patterns of schooling expectancy across 29 districts in Papua from 2010 to 2023 by combining probabilistic clustering with spatial visualization methods. Methods: The analysis applied Gaussian Mixture Model (GMM) clustering, which was validated using the Silhouette Index and Davies–Bouldin Index (DBI), to group districts based on HLS trends. Fourteen candidate probability distributions were evaluated using Kolmogorov–Smirnov and Anderson–Darling tests. In addition, five model selection criteria (AIC, BIC, AICc, CAIC, HQC) were applied to refine the fit. Cluster-wise mixture model was constructed, and spatial interpretation was improved through MATLAB’s Mapping Toolbox as well as wind rose diagrams. Results: During the process of the analysis, four statistically distinct clusters were identified. Cluster 3 (coastal districts) showed the highest and most stable HLS (12.1–14.0 years), while Cluster 4 (remote highlands) signified the lowest (2.4–5.6 years) with high dispersion. Right-skewed distributions (e.g., Weibull, Gamma) modeled high-performing districts, and heavy-tailed, left-skewed ones (e.g., Stable, Inverse Gaussian) modeled marginalized regions. Spatial visualization confirmed a clear coastal–highland divide in educational attainment. Conclusion: The proposed incorporation of probabilistic modeling and spatial clustering offered a robust analytical tool for capturing intra-regional educational disparities. This framework provided empirical evidence to support geographically differentiated policy interventions in Papua and could be adapted to similar underserved regions in future studies. Keywords: Schooling Expectancy, Gaussian Mixture Model, Probabilistic Modeling, Silhouette Index, Davies–Bouldin Index, Spatial Clustering, Education Inequality, Papua Province.
Hybrid Dual-Stream Deep Learning Approach for Real-Time Kannada Sign Language Recognition in Assistive Healthcare Hugar, Gurusiddappa; Kagalkar, Ramesh M.
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.393-406

Abstract

Background: Recent advances in sign language recognition (SLR) focus on high-resource languages (e.g., ASL), leaving low-resource languages like Kannada Sign Language (KSL) underserved. Edge-compatible, real-time SLR systems for healthcare remain scarce, with most existing methods (CNN-LSTM, 3D ResNet) failing to balance accuracy and latency for dynamic gestures. Objective: This research work aims to develop a real-time, edge-deployable KSL recognition system for assistive healthcare, addressing gaps in low-resource language processing and spatio-temporal modeling of regional gestures. Methods: We propose a hybrid dual-stream deep learning architecture combining EfficientNetB0 for spatial feature extraction from RGB frames. A lightweight Transformer with pose-aware attention to model 3D hand keypoints (MediaPipe-derived roll/pitch/yaw angles). We curated a new KSL medical dataset (1,080 videos of 10 critical healthcare gestures) and trained the model using transfer learning. Performance was evaluated quantitatively (accuracy, latency) against baselines (CNN-LSTM, 3D ResNet) and in real-world tests. Results: The system achieved 97.6% training accuracy and 96.7% validation accuracy, 81% real-world test accuracy (unseen users/lighting conditions). 53ms latency on edge devices (TensorFlow.js, 1.2GB RAM), outperforming baselines by ≥12% accuracy at similar latency. The two-stage output pipeline (Kannada text + synthetic speech) demonstrated 98.2% speech synthesis accuracy (Google TTS API). Conclusion: Our architecture successfully bridges low-resource SLR and edge AI, proving feasible for healthcare deployment. Limitations include sensitivity to rapid hand rotations and dialect variations. Keywords: Assistive Healthcare, Edge AI, Kannada Sign Language, Low-resource Language, Real-time Recognition, Transformer.
Digital Transformation of Islamic Endowments (Waqf): What Appeals to Generation Z in e-Cash Waqf? Canggih, Clarashinta; Imron Mawardi; Zaimy Johana Johan; Yan Putra Timur
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.337-352

Abstract

Background: Cash waqf in Indonesia is optimized through the use of digital media to improve access, transparency, and public participation, particularly among the tech-savvy younger generation. This led to the formulation of effective strategies, which enabled the understanding of factors influencing digital waqf intention, including gender-based differences. Objective: This present study aims to explore gender differences in respect to the determinants of intention towards participating in digital cash waqf. This was realized by comparing responses between male and female Generation Z individuals. Methods: This quantitative study adopted purposive sampling method to collect data. Subsequently, a total of 645 respondent data were processed using Partial Least Square Structural Equation Model (PLS-SEM) method with the assistance of SmartPLS 4.0 software. Results: The male and female respondents stated that cash waqf literacy did not influence trust and behavioral intention. However, perceived ease of e-cash waqf significantly impacted both trust and behavioral intention. Majority of the male respondents reported that religiosity, and trust in nazhir had a significant impact. Both genders stated that religiosity did not moderate the relationship between the variables. Conclusion: In conclusion, the importance of technological ease of use and religiosity in influencing trust and intention to contribute to digital cash waqf was analyzed. Based on this perspective, both variables impacted trust and behavioral intention. The female respondents perceived trust as an insignificant factor, and recommended nazhir institutions partnered with financial technology (fintech) companies to develop user-friendly platforms. This included the engagement of female donors through religious education. The numerous campaigns should focus on technological literacy and the religious value of digital waqf contributions. Keywords: E-cash waqf, Generation Z, Multi Group Analysis, Male, Female
Generating User Personas for Eliciting Requirements Using Online News Data Awalurahman, Halim Wildan; Raharjana, Indra Kharisma; Kartono, Kartono; Fauzi , Shukor Sanim Mohd
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.407-419

Abstract

Background: In software development, creating user personas remains challenging despite their recognized value. Cost, adaptability, and data scarcity present obstacles in designing these critical personas. A new perspective and process innovation for generating user personas is essential to overcome this hurdle.  Objective: This study introduces a method for extracting user persona attributes, including names, occupations, workplaces, and goals.  Methods: A framework for extracting user persona information from online news sources is created. Our method employs a comprehensive SpaCy processing pipeline, incorporating NER, SpaCy rule-based matching, and phrase matching.  Results: The evaluation results showcase promising performance metrics, with an average recall value of 0.700, precision of 0.402, and F1-score of 0.506.  Conclusion: This study demonstrates the feasibility of extracting user persona attributes from online news data. Future research could focus on enhancing the method’s performance, investigating its effectiveness in creating relationships, and ensuring that the generated user personas accurately reflect the news text data.  Keywords: Process innovation, Natural Language Processing, Online News, Software Development, User Persona 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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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Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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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Abstract

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

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