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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 23 Documents
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Search results for , issue "vol. 12 no. 2 (2026): june" : 23 Documents clear
Exploring Factors Influencing User Avoidance Behavior Toward QR-Based Payment Scams Angelique Aurielle Alpaullivarez; Ahmad Nurul Fajar
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.458-473

Abstract

Background: QR Code Phishing (Quishing) is one of the phishing threats using QR codes that are currently rampant in the use of QR-based payment systems as a digital payment tool. Research related to avoiding Quishing threats in QR-based payment systems is still limited, where previous research has discussed more about phishing threats. Objective: This research aims to determine the factors that influence QR-based payment users avoidance behavior towards Quishing threat by using the Technology Threat Avoidance Theory (TTAT) model. Methods: This research collected data via Google Form questionnaire from 469 QR-based payment users in the Jakarta Municipality area, Indonesia through purposive sampling. The data was then analyzed using the Partial Least Square Structural Equation Model (PLS-SEM) method. Results: The results show that 10 of the 11 hypotheses are accepted.  Perceived Severity (SEV), Perceived Susceptibility (SUS), and Distrust Propensity (DIST) significantly influenced Perceived Threat (THR). Meanwhile, Risk Propensity (RSK) has no influence on Perceived Threat (THR). Perceived Threat (THR), Impulsivity (IMP), Safeguard Effectiveness (EFF), Safeguard Cost (CST), Self Efficacy (SLF) significantly influenced Avoidance Motivation (MOT). Avoidance Motivation (MOT) has a significant influence on Avoidance Behavior (BEH). System Trust (ST) has a moderate effect on the relationship between Avoidance Motivation (MOT) and Avoidance Behavior (BEH). Overall, The TTAT model contributed 55.1% to explaining the influence of QR-based payment users avoidance behavior. Conclusion: This study shows that the avoidance actions of QR-based payment users in Indonesia are influenced by Avoidance Motivation. Avoidance motivation is influenced by Perceived Threat, Self Efficacy, Safeguard Effectiveness, Safeguard Cost, Impulsivity. Also, System Trust also significantly moderates its influence on avoidance behavior. This is a guide for QR service providers to improve security in QR-based payment transactions and preventive education steps for QR-based payment users to avoid the threat of Quishing.   Keywords: Quishing, Avoidance Behavior, Technology Threat Avoidance Theory, QR-based payment
Artificial Intelligence Technologies and the Performance of Nigerian Banks Ademola Samuel Sajuyigbe; Sunday Festus Olasupo; Oloruntoba Oyedele; Adeniran Rahman Tella; Adebanji Williams Ayeni; Matthew Olubayo Omotoso
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.196-209

Abstract

Background: The Nigerian banking industry has encountered substantial challenges and opportunities arising from globalization, with AI emerging as a critical enabler of operational efficiency, customer service excellence, and improved financial performance. Objective: This study investigated the impact of AI technologies on the performance of Nigerian banks. Methods: The study drew data from 228 respondents across six selected banks through a structured questionnaire. Path Analysis-Structural Equation Modelling (PA-SEM) was employed for data analysis using STATA version 15. Results: The findings indicate that AI-driven applications—such as chatbots, personalized banking services, and automated loan processing—substantially enhance customer satisfaction and deposit mobilization. The results underscore the pivotal role of AI technologies in strengthening the performance of Nigerian banks amid globalization, positioning the sector for sustained growth and development. Conclusion: The findings highlight the transformative role of AI technologies in the global banking landscape, emphasizing their significance in enhancing efficiency, customer engagement, and overall sector growth. As AI advances, its strategic adoption will be essential for maintaining a competitive edge and supporting the future expansion of the Nigerian banking industry.   Keywords: Chatbots, Personalized banking Services, Automated loan processing, AI JEL Classification: C45, E50, G21, J24
Empowering Machine Learning for Virtual And Augmented Reality in Education: A Systematic Review Of Current Trends and Techniques Fuad Manna Fataftah; Siti Hazyanti Mohd Hashim; Mohammed Wedyan
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.300-319

Abstract

Background: Machine learning (ML) integrated with virtual reality (VR) and augmented reality (AR) is also becoming widespread in education, especially in the STEM and medicine fields. The technologies have proved to be promising in improving learners’ engagement, learning capabilities, and practical skills with the help of immersive experiences. However, the overall picture of the effects of ML-based VR and AR devices on pedagogical outcomes has not been fully studied. Objective: This study aimed to conduct a systematic literature review to understand how ML-integrated VR and AR technologies are used in the educational process, how effective they are in enhancing learning outcomes,and the challenges hindering their implementation. Methods: A Preferred Reporting Items for Meta-analysis (PRISMA)-based systematic literature review was conducted. Database searches identified 210 records. In the final qualitative synthesis, 22 peer-reviewed articles were included in the screening and eligibility test. The themes used in the analysis of the studies included trends in publications, uses of technologies, effects of learning, user experience, theory of use, implementation issues, and perspectives on future research. Results: VR and AR led to improvements in conceptual understanding, problem-solving, and engagement of learners. ML applications include adaptive learning and other applications in biometric feedback systems and intelligent tutoring. However, the main obstacles were high prices, technical nature, and lack of transfer of skills. A small sample size and context-specific findings were reported in most of the studies. Conclusion: The review established the pedagogical promise of integrating ML-VR and AR but emphasized the need for scaled, all-inclusive, and cross-disciplinary applications. The research should be further enhanced in the future with the longitudinal effects of AI, the model of AI personalization, and learner types to enhance the learning potential.   Keywords: Machine Learning, Virtual Reality, Augmented Reality, Education Technology, Immersive Learning, Systematic Review
Improving SAM 2 for Agricultural Land Segmentation through Fine-Tuning, Point Prompt Augmentation, and Negative Prompt Calibration Yayang Setia Budi; Fardan Al Jihad; Nurjannah Syakrani; Trisna Gelar; Muhammad Rizqi Sholahuddin; Djoko Cahyo Utomo Lieharyani
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.210-222

Abstract

Background: The Segment Anything Model 2 (SAM 2) represents a state-of-the-art foundation model for object segmentation; however, its application to satellite-based agricultural mapping faces significant challenges. Standard SAM 2 architectures often struggle with the spectral ambiguity of fragmented tropical landscapes and the domain gap inherent in remote sensing imagery. Furthermore, the model’s interactive nature requires precise spatial guidance, making it sensitive to both the location and density of input prompts, which limits its scalability for automated large-scale monitoring. Objective: This study aims to (1) analyze the impact of domain-specific fine-tuning combined with automated Point Prompt Augmentation (PPA) and Negative Prompt Calibration (NPC) on segmentation accuracy; (2) evaluate the performance of four SAM 2 variants (Tiny, Small, Base+, and Large) to identify the optimal backbone for agricultural tasks; and (3) determine the optimal prompt density for both positive and negative points. Methods: The SAM 2 variants were fine-tuned using the LoveDA satellite dataset. Evaluation was conducted through an automated pipeline comparing two initialization strategies: Largest Agricultural Area (LAA) Centroid and random placement. The study implemented PPA to strategically increase positive prompt density and NPC to suppress "mask leakage" into irrigation infrastructure. Performance was quantified using mean Intersection over Union (mIoU) and Jaccard & F-measure (J&F) metrics. Results: The Small variant emerged as the superior backbone, achieving a peak mIoU of 0.7255 and J&F of 0.7734, representing a significant improvement over the pretrained baseline. The results indicate that the LAA Centroid strategy provides a more stable spatial anchor, while the integration of three positive and three negative points optimized the boundary alignment. The Small variant maintained a high computational efficiency with an average inference time of 2.62 minutes. Conclusion: Domain-specific fine-tuning, coupled with the proposed PPA and NPC frameworks, successfully mitigates the limitations of SAM 2 in agricultural remote sensing. This research provides a robust methodology for automated, high-precision land segmentation, bridging the gap between foundation models and specialized geographic information systems.   Keywords: Agriculture Segmentation, Satellite Imagery, Segment Anything Model 2, Fine-tuning, Point Prompt Augmentation, Negative Prompt Calibration
Real-Time Meat Authentication Using Deep Convolutional Neural Network with ResNet-50 Architecture Adhi Kusnadi; Rangga Winantyo; Fenina Adline Twince Tobing; Muhammad Tanveer; Ghulam Jilani Waqas
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.320-332

Abstract

Background: Many issues arise from meat adulteration with regard to food safety and halal certification. Because pork is so visually similar to beef, removing or identifying either type of product becomes almost impossible without some sort of testing. Unfortunately, for many situations, traditional methods for testing authenticity are typically lengthy, costly, and impractical. Objective: This study utilizes computer vision to authenticate beef, pork, and mixtures of both using a deep learning approach. The method used consisted of preprocessing the photographs and performing various forms of data augmentation to increase the dataset’s representative and generalizing capabilities. Methods: We achieved our highest classification accuracy through transfer learning using ResNet-50 as the primary model used for classifying the different types of meats covered by the study, providing empirical metrics and K-fold cross-validation to support reliable classification results. A comparative evaluation using MobileNetV2, EfficientNetB0, and InceptionV3 was also conducted to validate the effectiveness of the proposed ResNet 50 model. Results: The experiment showed that the proposed ResNet-50 model could classify images into their respective classes with a high level of accuracy (98.33%) and greater than 97% for both precision and recall across all classes, in addition to an average inference time of 0.124 s per image. Furthermore, the 5-fold cross-validation yielded an average classification accuracy of 99.33% ± 0.37%, indicating that the performance was stable across multiple data partitions. The low inference time demonstrates the feasibility of the proposed model for real-time meat authentication applications on mobile devices. Conclusion: Overall, the findings indicate that the proposed approach is effective for meat product verification and that computer vision solutions can support the development of practical food safety and halal verification applications.   Keywords: Meat authentication, Deep learning, Convolutional Neural Network, ResNet-50, Real-time classification, Food safety
Lightweight Skeleton–Based Hand Gesture Recognition Using Machine Learning for Human-Robot Interaction Panca Mudjirahardjo; Rahmadwati; Angger Abdul Razak; Raden Arief Setyawan; Tanjo Yui
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.223-235

Abstract

Background: Gesture-based control has emerged as a natural and intuitive approach for human–robot interaction (HRI). Advances in computer vision, particularly skeletal pose estimation, provide robust solutions to hand gesture recognition that are independent of lighting conditions and other sensor dependencies. However, most previous studies have relied heavily on deep neural network approaches, such as graph convolutional networks (GCN) and transformer-based architectures, which require substantial computational resources, making them less suitable for real-time robotic system applications. Objective: This study aims to develop a lightweight skeleton pose-based gesture recognition framework for human – robot interaction, focusing on accuracy, robustness, and computational efficiency. Methods: In this experiment, hand movement reference points were extracted using skeleton pose estimation. From these reference points, referred to as joint coordinates, feature vectors are constructed. These feature vectors are then used as input to the ML model, including SVMs, RF, GBM, and LGBM. The machine learning models were comparatively evaluated in terms of F1-score recognition, latency, and robustness in dynamic environments. Results: The SVM classifier consistently outperformed RF, GBM, and LGBM, achieving an F1-score of 91.3%. Real-time processing with an average latency of 44 ms per frame (≈22.7 FPS) and demonstrated stable performance under dynamic operating conditions. These results indicate that the selected skeleton keypoints and feature representation are effective in capturing discriminative hand-gesture patterns while maintaining low computational overhead. Conclusion: When combined with conventional machine learning classifiers, this study demonstrates that efficient skeleton pose-based feature construction offers a viable alternative for lightweight computation in gesture-based human – robot interaction. This study’s findings indicate that reliable and responsive gesture-based control supports practical deployment on embedded humanoid robot platforms.   Keywords: Skeleton-based gesture recognition, human – robot interaction, machine learning, computer vision, embedded robotics
Automatic Reading of Analog Gauges Using YOLOv9, Segment Anything Model, and TrOCR Diah Asmawati; Chastine Fatichah
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.333-347

Abstract

Background: The main challenge in this research is the vulnerability of manual gauge readings to human error, particularly under degraded visual conditions such as lighting distortion, tilted perspective, and blurred images. Furthermore, achieving fully automatic gauge reading remains challenging because many existing approaches still depend on manually predefined scale ranges, perspective-correction preprocessing, or limited gauge configurations. Objective: This study aims to automate the reading of analog gauges, which are widely used across various industrial sectors. Methods: This study proposes an automatic analog gauge-reading framework based on YOLOv9, SAM, and TrOCR. YOLOv9 was used to detect the face of the analog gauges and their components in the image, followed by prompt-guided SAM segmentation to extract the major and minor needle and scale ticks. Meanwhile, the scale-number recognition is performed by the TrOCR. Unlike several previous approaches, the proposed method calculates gauge values through ellipse-based angular mapping without adding specific steps for prospective correction and manually providing initial scale ranges. Then, this approach was evaluated using datasets consisting of analog gauge images from various sources and representing several instrument types. Result: The proposed system achieved an average relative error (ARelE) of 1.935% and an average reference error (ARefE) of 0.601%. Furthermore, the robustness evaluation of this framework yielded stable performance across several degraded visual variations. Conclusion: The main contribution of this study is the integration of PGS, transformed-based OCR, and ellipse-based interpretation into a single pipeline for adaptive analog gauge reading. This study presented potential industrial monitoring solutions, in environments that require automation and contactless measurement.   Keywords: Analog Gauge, Ellipse Fitting, SAM, TrOCR, YOLOv9
Transitioning to Digital Currency: Factors Influencing Public Acceptance of the Digital Rupiah Nusandika Patria; Made Harta Dwijaksara; Setiadi Yazid
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.534-547

Abstract

Background: The transformation of payment systems from cash-based transactions to digital platforms has significantly influenced the financial behavior of Indonesian society. A major innovation in this transition is the development of the Digital Rupiah, a Central Bank Digital Currency (CBDC) initiated by Bank Indonesia. Although global studies on CBDC adoption are emerging, limited research has examined its acceptance in Indonesia, particularly among technologically literate users, such as cryptocurrency customers. Objective: This study focuses on public acceptance of the digital rupiah and examines how trust and innovativeness serve as additional determinants beyond the core constructs of the UTAUT. Methods: A quantitative research design was employed using scanning electron microscopy-PLS. The study involved 400 respondents who are active Indonesian cryptocurrency users. The relationships between UTAUT variables and extended constructs were assessed using data derived from systematically designed questionnaires. Results: Performance expectancy, effort expectancy, social influence, trust, and innovativeness have a significant impact on behavioral intention to use digital rupiah, whereas facilitating conditions and behavioral intention directly influence use behavior. Importantly, the inclusion of trust and innovativeness as additional constructs revealed their substantial contribution in shaping attitudes and intentions toward adopting CBDC-based financial technologies. Conclusion: The following factors play vital roles in the acceptance of the digital rupiah. These insights provide valuable guidance for policymakers, particularly the Bank of Indonesia, in designing effective communication and socialization strategies to foster adoption. Future studies should further examine user acceptance across different demographic groups and broader segments of Indonesian financial technology users.   Keywords: Digital Rupiah, Central Bank Digital Currency (CBDC), UTAUT, Trust, Innovativeness, SEM-PLS
Toward Real-Time Hoax Detection: Integrating Transformer for News Scraping and Semantic Analysis M. Adnan Nur; Herlinah; Sitti Zuhriyah
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.348-360

Abstract

Background: Social media has become one of the primary sources of information for the public, but it is also vulnerable to fake news (hoaxes). Various supervised learning approaches have been used for hoax detection; however, they generally depend on labeled data availability and require retraining to handle new claims. Objective: This study implements and evaluates a zero-shot inference-based hoax verification pipeline capable of verifying claims in near real-time without model retraining. Methods: The study uses a computational experiment approach consisting of four stages: (1) construction of a test claim dataset through paraphrasing using the NLLB-200, T5, and mT5 models; (2) keyword extraction using KeyBERT with IndoBERT embeddings and parameter optimization; (3) news article summarization using IndoBART; and (4) semantic similarity analysis by comparing Multilingual-E5, BGE, LaBSE, SBERT, IndoBERT, and TF-IDF as the baseline. Results: In the keyword extraction stage, the best balance between relevance, redundancy, and keyword coverage was achieved using the hybrid configuration of Top-k = 12, Top-p = 0.85, MMR = 0.6, and Ctx = 2. In the verification stage, Multilingual-E5 delivered the highest performance, achieving an accuracy of 0.96 and an F1-score of 0.94. In contrast, IndoBERT produced the lowest results, particularly for paraphrased claims. TF-IDF achieved good performance on original claims but experienced a decline when semantic variations were handled. Article summarization helped reduce article length, although this stage removed part of the information relevant to the verification process in some cases. Conclusion: The results of this study show that the zero-shot inference-, retrieval-, and semantic similarity-based approaches are capable of verifying hoaxes with high accuracy while maintaining stable performance across various claim variations. This approach offers greater flexibility for handling new claims than supervised learning methods and is suitable for application in near real-time hoax verification systems.   Keywords: hoax verification, keyword extraction, semantic similarity, summarization, zero-shot inference
Assessing Vegetation Loss in Emerging Industrial Zones through Spatio-Temporal ANN Classification of Sentinel-2 Data Nahdah Ghina Handayani; Achmad Fauzan
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.283-299

Abstract

Background: Land-use change has been developed as a critical environmental issue, primarily driven by population growth and the expansion of industrial zones. However, specific studies that examine the dynamics of land-cover change surrounding newly developed industrial areas, such as the Kendal Industrial Park (KIP), remain limited in the Indonesian context. Objective: This study aims to analyze land-cover changes in the vicinity of the KIP during the 2019–2023 period and to quantify the proportions of the transitions. Methods: This study adopted Sentinel-2 Level-2A surface reflectance imagery from the harmonized COPERNICUS/S2_SR collection. Land cover was classified using an Artificial Neural Network (ANN) with a binary sigmoid function and evaluated through standard metrics, followed by land-use change analysis across multiple buffer radii and village scales. Results: ANN showed consistently strong performance across all scenarios, achieving accuracy and AUC values of 0.83–0.85. Furthermore, the 75:25 train–test split provided the most balanced and generalizable results across replications. Spatio-temporal analysis indicated higher vegetation density within the 0.5–1 km core radius, a sharp decline in the 2–4 km transition zone, and a slight recovery at 6–7 km. Kumpulrejo Village also recorded the highest vegetation loss of –3.55% due to rice field conversion into industrial and residential areas. Generally, consistent 2019–2023 patterns identified the transition zone as the most vulnerable area to vegetation degradation. Conclusion: ANN effectively captures spatio-temporal land-cover dynamics, where industrial expansion drives vegetation loss, particularly within the 2–4 km transition zone. The results provide empirical evidence of the trade-off between economic growth and environmental sustainability, addressing a gap by examining newly developed industrial regions. Future studies should compare ANN with other deep learning models and integrate socio-economic data to better explain land-conversion drivers and enhance generalizability.   Keywords: Industrial Development Impact, Kendal Industrial Park (KIP), Land Use Change, Spatio-Temporal Classification, Vegetation Loss

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