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INDONESIA
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 246 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 

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