Journal of Information Systems and Informatics
Journal of Information System and Informatics
Asosiasi Doktor Sistem Informasi Indonesia
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Post-Quantum Migration in the Financial Sector: A Systematic Review of Readiness, Risks, and Transition Frameworks
Quantum computing threatens the classical cryptographic systems underpinning financial infrastructure, exposing payment platforms, interbank networks, and digital services to Harvest-Now-Decrypt-Later (HNDL) risks. This study systematically reviews quantum threats and post-quantum cryptographic readiness in the financial sector to assess preparedness, identify implementation challenges, and synthesize migration pathways aligned with emerging standards. A PRISMA-guided review of 17 peer-reviewed studies published between 2020 and 2025 was conducted, examining quantum threat models, post-quantum cryptographic schemes, quantum key distribution architectures, and sector-specific deployment barriers. The review finds that lattice-based schemes, especially CRYSTALS-Kyber and CRYSTALS-Dilithium, are the leading candidates for financial adoption, while hybrid cryptographic approaches offer the most feasible transition strategy. However, the current evidence base is predominantly simulation-driven, with limited real-world deployment and validation. The study provides a sector-specific synthesis of quantum threats, post-quantum readiness, and migration pathways in financial systems, and advances an integrated readiness and migration framework based on cross-study thematic analysis.
Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
Data-Driven Traffic for Infrastructure Planning: An LSTM Approach Using Indonesian Road-Vehicle Trends
The rapid growth of motorized vehicles in Indonesia, unmatched by proportional expansion in road infrastructure, has intensified pressure on the national transportation system. This study examines the application of a Long Short-Term Memory (LSTM) model to analyze and forecast the national traffic load ratio, defined as the ratio of total motorized vehicles to total road length. Annual aggregate data from the Indonesian Central Bureau of Statistics (BPS) for the period 2016–2023 were used in the analysis. The results indicate that the model achieved a strong fit on the training data, with RMSE = 0.3652 and MAE = 0.3617, but performed substantially worse on the test data, with RMSE = 1.7585 and MAE = 1.7585. This discrepancy suggests overfitting, largely attributable to the extremely limited sample size. As such, the findings should be interpreted as exploratory rather than as evidence of reliable forecasting performance. Despite these limitations, the model projects a continued upward trend in national infrastructure pressure over the next five years. These findings provide an initial data-driven indication that transportation infrastructure demand in Indonesia is likely to intensify, while also underscoring the need for future research using larger datasets and baseline model comparisons before policy-level application can be justified.
Analyzing Public Sentiment on the Proposal to Return Regional Head Elections to DPRD on Platform X Using the C4.5 Algorithm
This study examines public sentiment among X users toward the proposal to return regional head elections (Pilkada) to an indirect electoral mechanism through the Regional People’s Representative Council (DPRD), using a decision-tree classifier based on the C4.5 approach. A dataset of 4,127 tweets collected via X API v2 between December 2024 and January 2026 was analyzed using a seven-stage text preprocessing pipeline. Sentiment labels were generated through a hybrid lexicon-based approach, followed by manual verification of 500 stratified tweets by two independent annotators, yielding substantial inter-annotator agreement (Cohen’s Kappa = 0.78). TF-IDF was used for feature extraction, and the dataset was divided using an 80:20 stratified train-test split. The classifier achieved 81% accuracy, 82% precision, 79% recall, and an F1-score of 80%, outperforming Naive Bayes (74%) and Support Vector Machine (79%) baselines on the same dataset. The sentiment distribution showed that 45% of tweets were negative, 32% were positive, and 23% were neutral, indicating a predominantly critical response among X users toward the proposal. These findings describe discourse on X during the study period and should not be interpreted as representative of broader public opinion. Overall, the study highlights the potential of machine learning methods for analyzing Indonesian political discourse on social media.
Institutional and Individual Drivers of AI Adoption in Higher Education: An Integrative TAM–TOE Model
The rapid diffusion of artificial intelligence (AI) in higher education necessitates a deeper understanding of both institutional and individual factors influencing its adoption, particularly in developing-country contexts. This study examines the drivers of AI adoption in Indonesian higher education institutions by integrating the Technology Acceptance Model (TAM) and the Technology, Organization, Environment (TOE) framework. Addressing a gap in prior research that often separates individual acceptance from institutional readiness, this study adopts a quantitative survey approach involving 366 academic stakeholders, including lecturers, students, and administrative staff. Data collected between October and December 2025 were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived ease of use strongly influences attitude toward AI, which in turn significantly affects behavioral intention. Perceived usefulness also has a positive, albeit weaker, effect on behavioral intention. At the institutional level, environmental context is found to significantly influence AI readiness, while other contextual factors exhibit limited explanatory power. Several hypothesized relationships, including the effects of AI readiness on perceived usefulness and the moderating roles of digital literacy and top management support, are not supported. These results suggest that AI adoption in higher education is primarily shaped by user-centered factors, while institutional readiness may depend on additional determinants not fully captured in the model. This study provides empirical insights into the role of AI readiness as an intermediate construct within an integrated TAM–TOE framework in higher education.
CultureFun: An Interactive Web-Based Cultural Learning Platform Using a Human-Computer Interaction Approach
This study aimed to develop CultureFun, a user-centered web-based platform for cultural learning among younger generations. The study addressed the limitation of existing digital cultural learning media, which often rely on text-heavy content and provide limited interactivity. User-Centered Design and Design-Based Research were applied to guide needfinding, empathy-based analysis, iterative prototyping, and usability evaluation. Data were collected through semi-structured interviews and direct observation involving twelve participants aged 10–23 years. The final prototype was evaluated using the System Usability Scale. The results showed that CultureFun was perceived as easy to use and engaging, particularly because the platform presented short cultural content supported by images, quizzes, and mini-games. The platform achieved an average usability score of 72.08, indicating good and acceptable usability. This study concludes that integrating user-centered analysis, iterative prototyping, and gamified interaction can support the development of an effective and usable digital cultural learning platform. However, the findings were limited by the small sample size and the focus on usability rather than direct measurement of learning effectiveness. Future studies are recommended to involve more diverse participants and examine long-term cultural knowledge retention.
A Multi-Algorithm Approach for Predicting OSCE Exam Passing Status
This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams. Five machine learning algorithms Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (kNN) are assessed experimentally in this study. A dataset of 439 clinical competency data from Aisyah Pringsewu University midwifery students was used to create the model. Eight clinical skill factors were used as input, including baby massage, newborn care, and family planning services. To guarantee result stability, the 5-fold cross-validation approach was used for model validation. According to the test findings, every algorithm performs well, with an accuracy of more than 90%. On this particular dataset, SVM achieved a 100% classification accuracy, whereas Random Forest and SVM showed the most efficacy. With an average validation accuracy of 95%, neural networks also demonstrated excellent performance. This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams.
Real-Time Explainable Concept Drift Detection for Eco-Driving in Mining Trucks using KSWIN and Event-Triggered SHAP
Fuel consumption represents a significant operational cost in mining, where real-time eco-driving optimization is hindered by dynamic and non-stationary operating conditions. Variations in operator behavior and environmental factors often induce concept drift, which diminishes the reliability of static machine learning models and constrains the effectiveness of conventional drift detection methods. This study proposes a distribution-aware, event-triggered Explainable Artificial Intelligence (XAI) framework for detecting and diagnosing fuel consumption anomalies in streaming telematics data. A Hoeffding Tree Regressor was evaluated using a prequential scheme on 1,927,867 real-world observations, achieving a Mean Absolute Error (MAE) of 19.43 under non-stationary conditions. Concept drift was monitored using the Kolmogorov–Smirnov Windowing (KSWIN) algorithm, which detected 1,874 drift events. Upon detection, an event-triggered SHAP module identified contributing factors, indicating that behavioral features such as engine speed and accelerator position were dominant contributors in early drift events. The primary contribution of this study is the integration of distribution-based drift detection with event-triggered explainability within a unified streaming framework, facilitating both anomaly detection and interpretable root-cause analysis.
Data Mining Analysis for KIP Scholarship Eligibility Using Integrated DBSCAN and TOPSIS
This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.
Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation
Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.
Detecting Deceptive Online Reviews Using a Semantic Reliability Index and Hybrid Text Representation
Online review platforms such as Yelp play an important role in consumer decision-making, but the growing prevalence of fake reviews undermines their reliability. This study proposes a hybrid approach for fake review detection by integrating stylometric features, language model signals, and semantic embeddings within a unified classification framework. The proposed method combines linguistic indicators, including GPT-2 perplexity, lexical diversity, sentence burstiness, punctuation ratio, and sentiment intensity, with TF-IDF representations and Sentence-BERT embeddings. A composite feature, namely the Semantic Reliability Index (SRI), is introduced to capture interactions between semantic similarity and linguistic characteristics, serving as an auxiliary feature within the hybrid model rather than a standalone classifier. Experiments on a Yelp hotel review dataset demonstrate that the hybrid model outperforms baseline methods in terms of F1-score and AUC, indicating improved discriminative capability. It should be noted that the classification setting is based on a binary transformation of ordinal labels, which may simplify the underlying label structure and influence performance interpretation. Overall, this work's contribution lies in a systematic feature-integration strategy that enhances fake review detection in the evaluated dataset.
ACTE: A Pilot Feasibility Evaluation of a Mastery-Aware Task Recommender for Mobile Language Learning in Real-World Contexts
Mobile-assisted language learning (MALL) apps often present generic activities that ignore the semantic meaning of real-world places and provide limited skill-specific, mastery-based progression. This pilot feasibility study introduces the Adaptive Contextual Task Engine (ACTE), a lightweight on-device recommender that personalizes tasks using location semantics, CEFR-aligned modules, mastery status, and performance timing. ACTE was evaluated with 10 university students aged 18–23 in a simulated café environment to balance ecological validity and experimental control. Participants completed three A2 speaking tasks, the System Usability Scale (SUS), and a five-item relevance questionnaire. Results showed a mean SUS score of 72.0, exceeding the benchmark of 68. Participants rated task appropriateness for the café at 4.3/5 and real-life usability at 4.7/5, while 90% agreed that the tasks reflected authentic language use. Qualitative feedback confirmed contextual authenticity but indicated the need for clearer scoring explanations. These findings suggest that ACTE offers a practical, privacy-conscious, and replicable framework for situated MALL by linking semantic place affordances with mastery-based progression in controlled real-world simulations.
mobile-assisted language learning (MALL)
Ensemble Learning for Pediatric Stunting Detection: A Comparative Study of XGBoost, Random Forest, and LightGBM with Oversampling Techniques
Stunting, driven by chronic childhood malnutrition, remains a critical global public health concern. Early detection is persistently challenged by class imbalance in pediatric health datasets and the absence of systematic comparisons between oversampling strategies and ensemble classifiers. This study develops and evaluates an ensemble learning pipeline for stunting detection, benchmarking XGBoost, Random Forest, and LightGBM across five oversampling configurations — Original, SMOTE, ADASYN, Borderline-SMOTE, and SMOTE-ENN — using 10,000 pediatric health records from posyandu activities in Bangka Belitung Province, Indonesia. Seven anthropometric and demographic features were utilized, with stratified 80:20 train-test splitting and five-fold cross-validation. XGBoost with original imbalanced data achieved the highest Recall (0.9573) and a competitive F1-Score (0.9158), while LightGBM with SMOTE delivered the strongest balanced performance (F1-Score: 0.9160, ROC-AUC: 0.8431). SMOTE-ENN consistently underperformed across all classifiers. To our knowledge, this is the first study to simultaneously compare five oversampling strategies across three ensemble models within a unified framework, offering a foundation for high-sensitivity stunting surveillance in resource-constrained healthcare settings.
Reinforcement Learning–Guided Hyperparameter Tuning for U-Net-Based Super-Resolution of Brain MRI Under Synthetic Degradation
Low-resolution magnetic resonance imaging (MRI) may reduce visibility of fine anatomical details, motivating computational super-resolution (SR) to enhance perceived image quality. This study proposes an SR pipeline for 2D brain MRI images using a U‑Net baseline model and a reinforcement learning (RL) agent to automate hyperparameter tuning. Because the selected public dataset does not provide paired low-resolution/high-resolution (LR–HR) images, LR inputs are generated synthetically using a controlled degradation process (blur–downsample–upsample–noise), with deterministic degradation for validation and testing to ensure stable evaluation. The baseline U‑Net is trained using an L1 objective (optionally mixed with differentiable SSIM loss), AdamW optimizer, and ReduceLROnPlateau scheduler guided by validation PSNR. A Double Deep Q‑Network (Double DQN) agent then selects discrete action combinations of learning rate and SSIM-weighted loss mixing to fine-tune the baseline. For the held-out test set (n=60), the baseline improves degraded inputs from 27.04±3.21 dB to 30.10±3.59 dB PSNR and from 0.706±0.132 to 0.875±0.064 SSIM, respectively. RL fine-tuning yields a modest additional PSNR gain to 30.20±3.58 dB and SSIM remains comparable at 0.873±0.066. The paired statistical tests confirm that the PSNR improvement is significant (p<0.01), while changes in SSIM are not statistically significant, suggesting that for the tested synthetic degradation setting RL can provide reliable but incremental refinement when the baseline is already strong.
Comparative Performance Analysis of Dual-Prime RSA and Eight-Prime RSA
This study presents a comparative performance analysis of Dual-Prime RSA and Eight-Prime RSA by evaluating computational efficiency in key generation, encryption, and decryption at 1024-bit and 2048-bit key lengths. Experiments were conducted in a controlled environment, using processing time as the primary performance metric. The results show a consistent computational advantage for Dual-Prime RSA across all operations. At the 2048-bit key length, Eight-Prime RSA requires substantially more time for key generation, performing approximately 643% slower than Dual-Prime RSA, which highlights the overhead associated with increasing the number of prime factors. Decryption results further reinforce this gap: Eight-Prime RSA at 2048-bit records about a 247% increase in processing time compared with its own 1024-bit baseline and remains markedly slower than Dual-Prime RSA at the same key length. Although differences in encryption time are less significant, Eight-Prime RSA offers no meaningful efficiency advantage. While earlier studies suggest that additional prime factors may provide theoretical security benefits, this work is limited to empirical performance benchmarking and does not include a full security analysis. Overall, the findings indicate that Dual-Prime RSA is the more practical and scalable choice for real-world 2048-bit applications and performance-sensitive deployments.
Sentiment Analysis in Electronic Health Records for Patient-Centric Care: A Systematic Literature Review of Methods, Applications, and Challenges
This study examines the role of sentiment analysis in EHR narratives for enhancing patient-centred care, focusing on methodological approaches, application domains, and implementation challenges in clinical settings. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines. Relevant studies were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed. The search, conducted in September 2025, included peer-reviewed articles published between 2021 and September 2025. The findings reveal a clear shift from rule-based and traditional machine learning approaches to transformer-based models. Sentiment analysis is increasingly applied in areas such as mental health, oncology, and patient experience monitoring. However, most implementations remain domain-specific and are not fully integrated into routine clinical workflows. This study provides a structured synthesis of sentiment analysis in EHRs and identifies key gaps between methodological advancements and real-world implementation. It advances a socio-technical perspective that integrates analytical performance, clinical applicability, and governance considerations, offering a consolidated lens for understanding sentiment-aware healthcare systems. Despite rapid methodological progress, the impact of sentiment analysis in EHRs remains constrained by limited scalability and insufficient integration into clinical practice.
Clinical Natural Language Processing (Clinical NLP)
Security Analysis of Indonesian Region Government Web Applications Based on NIST SP 800-115 and WSTG v4.2
The rapid adoption of e-government systems has increased the exposure of government web applications to cybersecurity threats with the lack of security-focused implementation. Previous studies on web application security assessment commonly using automated vulnerability scanners or validated with another tools, which may produce false positives and fail to provide comprehensive insights. This research addresses this limitation by conducting a structured and multi-target security assessment of regional government web applications. The assessment integrates a systematic penetration testing process with comprehensive web application security testing guidelines. Automated scanning using OWASP ZAP and Arachni was combined with manual validation to ensure the accuracy of findings. The results identified nine validated vulnerabilities in the government portal and public service applications, and ten vulnerabilities in the legal documentation system. A significant portion of initial findings were confirmed as false positives after manual verification, highlighting the limitations of automated tools. The most common vulnerabilities were related to security misconfigurations, including missing security headers, outdated JavaScript libraries, and insecure cookie settings that highlight on weak in configuration hygiene and dependency management in this regional goverment. This study also demonstrates that combining structured penetration testing with detailed validation provides a more accurate and reliable assessment of government web application security.
A Lightweight Classical Machine Learning Pipeline for Rice NPK Deficiency Classification Using Hand-Crafted Feature Fusion
Many high-accuracy deep learning solutions for plant nutrient deficiency remain impractical in resource-limited settings due to computational cost and limited explainability. This study proposes a lightweight classical machine learning pipeline for rice leaf NPK (nitrogen, phosphorus, potassium) deficiency classification on the publicly available Kaggle Nutrient-Deficiency-Symptoms-in-Rice dataset (1,156 images); all results should be interpreted in this dataset context rather than as field-validated performance. The pipeline applies HSV-based leaf segmentation to reduce background influence. It extracts a 126-dimensional feature set combining masked color moments, HSV histograms, vegetation indices, LBP and GLCM texture descriptors, and spatial symptom ratios. Hyperparameters are tuned via RandomizedSearchCV with 5-fold StratifiedKFold and macro-F1 scoring; final evaluation uses a held-out 80/20 stratified test set kept separate throughout tuning. XGBoost achieves the best test performance (accuracy 0.9267; macro-F1 0.9233), followed by SVM-RBF (0.9224; 0.9187) and Random Forest. Feature importance analysis confirms that color moments dominate class separability, with texture and spatial features providing complementary support. The dominant remaining error is phosphorus–potassium confusion. The novelty lies in integrating leaf-focused preprocessing with a structured, low-cost feature representation suitable for mobile or edge deployment.
Federated Learning for Privacy-Preserving Sentiment Analysis in Distributed Electronic Health Record Environments: A Systematic Literature Review
Federated learning (FL) has emerged as a privacy-preserving approach for distributed healthcare analytics, yet its application to sentiment analysis of unstructured electronic health record (EHR) narratives remains limited. This systematic review examined the empirical maturity, methodological trends, and governance implications of federated sentiment-aware learning in distributed EHR settings. Following PRISMA 2020, searches were conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, and PubMed on January 5, 2026, covering peer-reviewed studies published from January 2021 to January 2026. After screening and eligibility assessment, 29 empirical implementation studies were included in the qualitative synthesis, while conceptual and survey papers were reviewed contextually but excluded from the core analysis. The evidence shows that FL in healthcare is advancing mainly in structured prediction and privacy-preserving infrastructure. By contrast, sentiment-aware learning on unstructured clinical narratives remains at an early stage, with limited implementation and validation. This review distinguishes empirical from conceptual contributions and proposes a governance-aware, literature-derived framework to guide future implementation-focused research.
Linking IT Governance to Organizational Performance in Higher Education: The Role of Digital Capability and Organizational Agility
This study examines how IT governance contributes to organizational performance through information systems (IS) success, digital capability, and organizational agility in higher education institutions. It addresses a critical gap by exploring why improvements in IT governance and system quality do not always translate into higher organizational capabilities and performance. A quantitative survey was conducted with 342 respondents from higher education institutions in Nusa Tenggara Barat (NTB), Indonesia, and analyzed using PLS-SEM. The results show that IT governance significantly improves system, information, and service quality. However, these dimensions do not significantly influence digital capability, and digital capability does not significantly affect organizational agility. Instead, organizational agility is the only construct that significantly enhances organizational performance, while IT governance shows no direct effect. These findings suggest that IT value creation is not linear but depends on the organization’s ability to translate technological resources into adaptive capabilities. This study provides empirical evidence on the indirect role of IT governance and offers a contextual clarification of IS success and digital capability relationships within NTB higher education institutions.
Sequential Requirements Prediction in Synthetic Fintech-Like Backlogs Using an Interpretable Hybrid of Transition Rules and Transformer Models
Fintech software development is characterized by rapid product iteration and stringent regulatory requirements, resulting in changing requirements as an interrelated set rather than individual elements. In this research, Sequential Requirements Prediction is proposed as a decision support task in Requirements Engineering, where the time-ordered prefix of completed backlog items is used to predict the next likely canonical requirement type as Top-k ranked output. To mitigate noise and inconsistency in backlog data, an LLM-aided semantic normalization step maps diverse requirement descriptions to a closed set of fintech requirement types. The research compares an interpretable rule-based Markov-1 predictor with Transformer-based sequential predictors under a case-level time-aware split. The proposed method is evaluated on a synthetic fintech-like backlog dataset consisting of 900 cases, 5,252 events, and 18 canonical requirement types. The best-performing model, Transformer + normalization + augmentation (M4), achieved Recall@5 = 0.638889, MRR@5 = 0.536806, and NDCG@5 = 0.566667. These results surpassed the rule-based predictor and non-normalized Transformer model. In addition, augmentation further improved Recall@5 from 0.493056 to 0.527778 in the rare-type subset. These findings suggest the methodological promise of the proposed framework for sequence-aware and compliance-conscious backlog analytics in synthetic fintech-like settings.
A Hybrid Ensemble Stacking Framework Integrating Long Short-Term Memory and Random Forest for Bitcoin Price Forecasting
Bitcoin is a non-linear and non-stationary digital asset that has become a highly volatile asset challenging the usual prediction models. In this paper, the authors present a problem-specific Hybrid Ensemble Stacking approach, the proposed approach, which combines the benefits of Long Short-Term Memory (LSTM) in terms of capturing long-term temporal variations with the power of Random Forest (RF) to process complex technical characteristics. The model follows a two-tier structure with a split ratio of 90:10 using BTC/USD historical data of Yahoo Finance and Binance (20102025) to combine the predictions of base learners with the use of a Linear Regression meta-learner. Findings show that pure LSTM has a low RMSE and MAE, but the Hybrid model has the best Mean Absolute Percentage Error (MAPE) of 3.54%. This means that the stacking mechanism will provide a more balanced error percentage, that is, it will enhance stability in forecasting at the phases of price discovery. It is novel in the sense that it uses macro-technical indicators to stabilize predictions in the face of market anomalies as a stacking scheme. These results have real-life implications on developers of financial systems in creating consistent crypto-asset risk management instruments.
A Hybrid Certainty Factor–XGBoost Approach for Cyberattack Detection Using the TON_IoT Dataset
Computer networks are vital to modern organizations, yet growing digital dependence has increased both the frequency and complexity of cyberattacks. To address this challenge, this study proposes an interpretable cyberattack detection framework that combines rule-based reasoning with machine learning through a hybrid Certainty Factor (CF)–XGBoost model. The framework integrates CF confidence scores and XGBoost probability outputs within a meta-learning classifier, enabling strong predictive performance while preserving explainability. Experiments conducted on the TON_IoT dataset using an 80:20 stratified split demonstrate that XGBoost achieved the highest accuracy at 99.61%, followed closely by the hybrid model at 99.42%, whereas the standalone CF model reached 76.31%. Although the hybrid approach produced a slightly lower accuracy than XGBoost alone, it substantially enhanced interpretability by connecting predictions to explicit rule-based reasoning. This makes the proposed framework especially suitable for Security Operations Center (SOC) environments, where transparent decision-making is essential. Overall, the findings suggest that the hybrid CF–XGBoost model offers a practical and explainable solution for cyberattack detection, though further validation on more diverse datasets is necessary before real-world deployment.
Immersive Technologies in 5G-Enabled Networks: A Systematic Review of Publication Trends and Adoption Recommendations
Fifth-generation (5G) infrastructure is unlocking the transformative potential of immersive technologies across various sectors. This research explores the nexus between immersive technologies and 5G network deployments, based on trends in publications, factors in adoption, as well as policy aspects. Adopting a systematic approach, peer-reviewed publications were analyzed, considering only publications from the previous 10 years, i.e., 2015-2025. The PRISMA approach was used to identify research articles that resulted in 43 studies selected for the review. The analysis of reviewed literature shows a surge in publications after 2018, with healthcare, academia, and sectoral training being the predominant fields of application. The synthesis of existing studies further reveals that infrastructural barriers are the most frequently reported constraint to adoption, followed by technical limitations and device readiness challenges. These constraints are severe and endured in developing countries. The reviewed literature identifies approaches for the telecommunications industry and policymakers, such as selective 5G deployments across different organisations to enable experimentation. Notwithstanding 5G technological advancements, it is unclear how organisations would adapt to such advancements, a gap that makes it challenging to assess its wider sectoral contributions. There is minimal research focusing on 5G adoption barriers, a gap that points to a need for future research adopting different methodological approaches, to investigate opportunities alongside risks. This research contributes to the analysis by tracking trends and barriers, along with providing actionable avenues for scaling immersive applications in constrained-resource settings.
Time-Series Monitoring of Sentiment Dynamics in Reviews of Four Indonesian E-Wallet Applications Using a Hybrid TF-IDF and Bi-LSTM Framework
This study proposes a hybrid sentiment analysis framework to examine user perceptions of four Indonesian e-wallet applications using Google Play Store reviews. The framework combines TF-IDF features reduced through Truncated SVD with a Bidirectional Long Short-Term Memory (Bi-LSTM) model within a two-stage evaluation design consisting of holdout classification and external temporal inference. For supervised classification, 20,000 raw reviews were filtered and labeled using a rating-based strategy, resulting in 13,823 labeled reviews. Reviews with ratings of 4–5 stars were assigned to the positive class and 1–2 stars to the negative class; these labels should be interpreted as sentiment proxies rather than fully human-validated ground truth. A second dataset of 24,000 reviews was constructed for balanced cross-application temporal comparison across 2024–2026. On the holdout test set, the proposed model achieved an accuracy of 0.881, with macro-F1 and weighted-F1 scores of 0.881. Under the external temporal setting, DANA remained relatively stable, GoPay improved markedly in 2025 and remained high in 2026, ShopeePay showed a gradual decline, and OVO exhibited the strongest negative trend. These results indicate that the proposed framework is useful not only for supervised sentiment classification but also for structured temporal monitoring across e-wallet platforms.
Large Language Models for Intelligent Decision Support in Inventory and Supply Chain Operations: A Systematic Literature Review
Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), is increasingly explored to strengthen decision support in supply chain and inventory management by improving interpretability and access to analytics. However, prior work is scattered across optimization, simulation, logistics, and governance discussions, limiting clear system design guidance. This study conducts a Systematic Literature Review (SLR) following PRISMA 2020 across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar, yielding 200 records, of which 34 studies were included in qualitative synthesis. Results show that LLMs are predominantly positioned as orchestration and explanatory layers operating alongside structured components such as optimization solvers, simulation engines, and digital twins, rather than as autonomous decision-makers. Governance, organizational readiness, and trust emerge as central considerations for operational deployment. This review provides an evidence map linking LLM roles and integration architectures across supply chain and inventory contexts. While LLMs offer strong augmentation capabilities, direct empirical validation for specific contexts such as web-based inventory systems remains limited; design implications for such systems are derived from the broader corpus, underscoring the need for standardized evaluation benchmarks and targeted empirical studies.
Enhancing YOLOv12-Based Rice Leaf Disease Detection through Evaluation of Three Data-Split Scenarios
One of the most significant staple crops in the world is rice, and one of the main causes of the drop in agricultural yields is illnesses that affect rice leaves. To avoid large agricultural losses, early diagnosis of these illnesses is essential. The goal of this project is to use YOLOv12, the most recent deep learning-based object detection architecture, to create a rice leaf disease detection system. The model was trained using a dataset of 4,744 photos of rice leaves that included three disease classes: Leaf Blast, Brown Spot, and Bacterial Leaf Blight. Methods to boost variability and enhance detection performance, image preprocessing with data augmentation was used. Standard object detection criteria, such as mean Average Precision (mAP), precision, and recall, were used to assess the model. The YOLOv12 model was highly effective in detecting rice leaf illnesses. According to the experimental data, it achieved a mAP of 97%, a precision of 96%, and a recall of 96.5%. The use of YOLOv12's greater efficiency and quality in detecting small objects—which is essential for identifying illness symptoms on leaves—is what makes this study successful. These results lay the groundwork for upcoming precision agricultural real-time monitoring applications.
Sensor-Driven Nutrient Monitoring Using a Two-Layer Machine Learning Model for Sugarcane Fertilization Recommendation
The growth of sugarcane requires optimal environmental conditions and the availability of balanced nutrients. However, fulfilling nutrition is a challenge because it requires targeted observation. The study proposes a machine learning-based decision support model using a predictive empirical approach to monitor nutrient needs and recommend fertilizer dosages. The proposed approach integrates field data with a two-layer modeling framework to support fertilization decision-making. The classification model predicts the status of nutrient adequacy, while the regression model estimates the level of fertilizer application. The target label (y) is generated through feature extraction using a rule-based empirical formula derived from the threshold of agronomic parameters. The nutrients analyzed included macronutrients (nitrogen, phosphorus, potassium) and micronutrients (iron, zinc, copper). Model development involves selecting the best-performing algorithm using recall for classification and RMSE and R² for regression. The results of the cross-validation showed that the Gradient Boosting algorithm achieved the most consistent performance, with a recall of 0.99 during training and >0.98 in holdout testing. The regression model also showed low RMSE and high R² values, especially for micronutrient estimation. The proposed model contributes to data-driven fertilization optimization.
IS/IT Strategic Planning for Digital Maturity Development in an FMCG Skincare Enterprise
In this study, an IS/IT strategic plan will be developed for FMCG Skincare Enterprises, a firm that operates within the oral care and personal care sector, with a high regard for innovation in its products. This research adopts a qualitative descriptive case study methodology is employed, whereby the study deliberately narrows down the scope of investigation to a single organization. Within this framework, the Ward and Peppard model is used to examine the external and internal environments of the business, in order to determine the information needs of the organization. Besides, the McFarlan Strategic Grid is applied to categorize and prioritize IS/IT initiatives in terms of their strategic importance. Based on the results, it is evident that the firm enjoys numerous competitive advantages, such as having innovative products and employing shoppertainment marketing strategies using digital technology to widen the market coverage. Nonetheless, the results also point out some strategic problems that the organization faces, such as having low production capacity and being heavily reliant on a specific digital marketing channel, TikTok Shop. This study proposes a hybrid system development strategy method as the best way to incorporate digital systems. The IS/IT strategic roadmap proposed will enable the firm to improve its digital structure, increase efficiency, and ensure that it transforms into a competitive organization in the beauty industry through time.
Comparative Quality of Services and Resource Utilization Analysis of Free5GC and Open5GS in Resource-Constrained Private 5G Networks
This research compares the performance of two widely used open-source 5G core (5GC) platforms, Free5GC and Open5GS, in a resource-constrained private network environment. While previous studies have mainly focused on feature comparison or large-scale deployments, performance under limited computational resources has received less attention, particularly for small-scale enterprise use cases. In this work, both platforms are integrated with UERANSIM to emulate end-to-end 5G communication and evaluated under dynamic user equipment (UE) scaling. Each 5GC instance and simulator component is allocated one CPU core and 2 GB of memory. Performance is assessed using key Quality of Service (QoS) metrics, including throughput, latency, packet loss, and resource utilization (CPU and memory), under both TCP and UDP traffic. The results show that Open5GS consistently provides better performance than Free5GC. It achieves up to 10.58 Mbps throughput compared to 9.22 Mbps and maintains lower latency around 0.72–0.73 ms, while Free5GC reaches up to 1.20 ms as the number of UEs increases. In addition, Free5GC reaches high CPU utilization earlier under increasing load. These differences are mainly related to its microservice-based architecture, which introduces additional processing overhead.
Mapping the Research Domains of Digital Monitoring: A Systematic Literature Review and Taxonomy
Digital monitoring is increasingly central to modern information systems, enabling continuous observation of assets, processes, and services through real-time data collection. Although advances in analytics and machine learning support data-driven decisions, monitoring, analytics, and decision-making are still often developed in isolation, limiting effective integration. This study maps digital monitoring research, classifies monitoring characteristics, and identifies gaps in linking monitoring with decision-making. Using a PRISMA-based Systematic Literature Review of Scopus-indexed journal articles published between 2020 and 2025, 97 studies were selected and analysed through thematic synthesis. The review shows that digital monitoring spans nine major domains, with infrastructure, environmental, and manufacturing applications most dominant. The study’s main contribution is a multidimensional taxonomy that classifies monitoring approaches by monitoring object, mode, analytics type, application domain, and information system orientation. This taxonomy also positions digital monitoring within the evolution of information systems toward decision intelligence. Findings indicate that current research remains largely technical, relying mainly on descriptive and predictive analytics, while integration with decision intelligence is still limited. A notable gap appears in digital service contexts, especially proactive user-experience monitoring in Internet Service Providers (ISP).
Information System Audit in a Spare Parts Distribution Company Using the ITIL V3 Service Strategy Framework
Information system audits play an important role in evaluating the effectiveness, governance, and strategic alignment of IT services within organizations. This study assesses the capability level of IT service strategy implementation in a spare-parts distribution company using the ITIL V3 framework, with a focus on three Service Strategy sub-processes: Service Portfolio Management, Financial Management, and Demand Management. A qualitative case study approach was employed through semi-structured interviews and document analysis involving key personnel responsible for IT service operations. Capability assessment used a binary scoring method, where Fully Achieved was rated as 1 and Not Achieved as 0, and the aggregated results were mapped to capability levels. The findings show that the organization operates between Level 2 (repeatable) and Level 3 (defined), indicating that core processes have been documented and partially implemented, but are not yet fully integrated across functions. Key gaps were identified in internal coordination, management information, monitoring practices, and customer interface mechanisms. The study recommends strengthening service governance through clearer policies, better cross-functional coordination, and more integrated monitoring to improve IT service performance and business alignment.
AI-Based Assignment Marking in African Open and Distance e-Learning Institutions: A Systematic Review
The rapid growth of student enrolment in African Open and Distance e-Learning (ODeL) institutions has intensified pressure on assessment systems, particularly in assignment marking, moderation, and feedback provision. Artificial Intelligence (AI) offers a promising solution for improving the scalability, consistency, and timeliness of assessment processes. However, evidence on the implementation, effectiveness, and governance of AI-assisted assessment in African ODeL institutions remains fragmented. This study synthesised literature published between 2019 and 2025 to evaluate the extent to which African ODeL institutions have utilised AI techniques in assignment marking. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of major academic databases identified 18 studies that met the inclusion criteria. The review examined AI techniques used, assessment types, evaluation methods, and reported challenges. Findings show that Machine Learning (ML), Natural Language Processing (NLP), and generative AI are the most frequently applied techniques, mainly in text-based assessments such as essays and short-answer responses. Although studies report gains in grading efficiency, consistency, and feedback generation, adoption remains constrained by infrastructural limitations, fairness concerns, linguistic diversity, weak governance frameworks, and limited empirical validation. Sustainable implementation requires standardised human-AI workflows, robust evaluation frameworks, and clear ethical and regulatory guidelines.
AI in Cybersecurity: A Systematic Review and Conceptual Audit Model
Technological advances, particularly in Artificial Intelligence (AI), are accelerating digital transformation while increasing system complexity and exposure to sophisticated cyber threats. These developments challenge traditional cybersecurity audit approaches, which are largely periodic, retrospective, and focused on binary control checks. In response, the adoption of generative AI (GenAI) and predictive AI (PredAI) in cybersecurity auditing is becoming increasingly important. Although AI can improve audit intelligence, scalability, timeliness, and effectiveness, its use also raises concerns about transparency, governance, and auditor independence. This study employed a two-stage methodology. First, a systematic literature review following PRISMA examined studies published between 2021 and 2026, yielding 36 eligible articles. The review found that hybrid AI approaches dominate the literature (58.3%), followed by GenAI (25.0%) and PredAI (16.7%). Despite this growing interest, the literature gives limited attention to risk-based auditing approaches that move beyond binary control confirmation toward context-aware, intelligence-driven cyber risk assessment. Second, using Design Science Research, the study developed the conceptual Anti-Sheriff cybersecurity auditing model. The model shifts auditing from compliance-driven enforcement to intelligence-supported risk governance, enabling continuous auditing, better risk prioritisation, and stronger organisational cyber resilience.
A Systematic Literature Review of Motivation, Trust, and Purchase Intention in Live Shopping Commerce: Toward an Integrated S-O-R Conceptual Model
The rapid growth of live shopping highlights the complex psychological mechanisms driving purchase intentions. However, previous research remains highly fragmented, often examining motivational or trust aspects in isolation. This study addresses this gap by developing an integrated conceptual model linking motivation and trust to consumer purchase intentions. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines. From an initial retrieval of 210 records, studies were systematically screened for relevance and appraised for quality, resulting in 40 empirical articles (2017–2025) extracted from the Google Scholar database using Publish or Perish. While relying exclusively on Google Scholar is a methodological limitation, it provides a practical baseline for this review. The synthesized literature underscores the Stimulus-Organism-Response (S-O-R) framework's dominance. Findings indicate that social and hedonic motivations act as dominant stimuli triggering social presence. Crucially, this engagement builds multidimensional trust in the streamer, which is subsequently transferred to the platform and product, reducing uncertainty and driving purchases. The proposed model integrates these effective and cognitive pathways into an evidence-based framework. Ultimately, the study demonstrates that social interaction precedes cognitive trust formation, providing a structural baseline for future empirical validation in live commerce.
Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features
Heart disease remains one of the leading causes of mortality worldwide, making early detection essential for effective intervention. Heart Rate Variability (HRV) is widely used as a non-invasive marker for assessing cardiac conditions, and machine learning has shown potential in classifying heart diseases such as Sudden Cardiac Death (SCD) and Congestive Heart Failure (CHF). This study evaluates the performance of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) using 15-minute ECG signals comprising three 5-minute segments. The dataset consists of 53 subjects, generating 159 segments, including SCD, CHF, and Normal Sinus Rhythm (NSR). To prevent data leakage, a subject-wise split (80:20) is applied for training and testing. Two evaluation scenarios are considered: per-segment classification and combined 15-minute classification. Results indicate that SVM and DT achieve consistently high, stable performance with near-perfect accuracy, precision, recall, and F1-score, whereas KNN shows lower, more variable performance, particularly in segment-based analysis. The combined 15-minute approach provides more stable results, suggesting improved HRV representation and class separability. Although the results are promising, further validation with larger, more diverse datasets is required to ensure robustness and generalizability. This study highlights the potential of HRV-based machine learning while emphasizing the importance of appropriate temporal representation and rigorous evaluation design.
A Dependency- and Trust-Aware Task Scheduling Framework for Efficient Internet of Things Edge Systems
The rapid growth of the Internet of Things (IoT) has significantly increased the number of connected devices, generating massive volumes of data and placing substantial demands on edge and fog computing infrastructures. Traditional resource management approaches often overlook task dependencies, which can lead to inefficient resource utilization, increased execution delays, reduced reliability, and potential security risks in distributed IoT environments. To address these challenges, this paper proposes an improved dependency-aware task scheduling framework designed to operate between edge devices and edge servers. The framework employs directed acyclic graph (DAG) modeling to represent task dependencies and execution order, trust-aware node selection to avoid malicious, overloaded, or unreliable nodes, and Particle Swarm Optimization (PSO) to support adaptive resource allocation under dynamic and heterogeneous workloads. Experimental results demonstrate that the proposed framework achieves an average latency of 50 ms, throughput of approximately 500 transactions per second (tps), and a task completion rate of 98%. These findings indicate that the proposed approach outperforms conventional scheduling methods by improving latency, throughput, reliability, security, and overall task execution efficiency in IoT-enabled edge computing environments.
Coraza-Based WAF with OWASP CRS for SQL Injection in Multi-Domain Web System
This research aims to design and implement a Web Application Firewall (WAF) based on the OWASP Core Rule Set (CRS) to enhance web application protection against SQL Injection attacks. The study was conducted in the web environment of the State Polytechnic of Ujung Pandang, which has more than 80 active subdomains with uniform server configurations, mostly using vulnerable CMSs such as WordPress. The proposed solution integrates Coraza, a Go-based WAF engine, into the Nginx reverse proxy system. The system includes a web-based control panel, JSON-formatted logging, and Redis support for efficient traffic mapping and storage, enabling flexible management of multiple domains. A key contribution of this study is the implementation of a centralized WAF management approach capable of securing more than 80 subdomains within a unified configuration environment. Tests were carried out using five SQL Injection scenarios: URL parameters, form-data, x-www-form-urlencoded, JSON API, and automated tools such as SQLMap. Without WAF, all attacks successfully penetrated the system, whereas with WAF activated, all tested payloads were successfully blocked, manual and automated, was effectively blocked, indicating a significant improvement in defense capability. These results demonstrate that the developed WAF system provides strong protection against SQL Injection attacks and indicate strong potential for enhancing web application security against SQL Injection attacks.
Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection
Sleep disorders are health problems that can affect quality of life and have the potential to increase the risk of various chronic diseases. Therefore, a computational approach is needed to accurately and efficiently classify sleep disorders. The ANN model used has a two-layer hidden architecture with 128 and 64 neurons, respectively, and uses the ReLU activation function, equipped with a dropout layer to reduce overfitting. Three neurons with a softmax activation function make up the output layer, which produces probabilities for every class. To improve model performance, three feature selection methods were compared, namely Chi-Square, Information Gain, and Pearson Correlation. The test results showed that the ANN model without feature selection produced an accuracy of 89.3%. After feature selection, the model's performance improved significantly. The Chi-Square method produced 8 selected features with the highest accuracy of 97.3%, followed by Information Gain with 5 features and an accuracy of 97.3%, and Pearson Correlation with 3 features and an accuracy of 88.0%. The results of this study demonstrate that selecting appropriate features can significantly enhance an ANN's ability to categorize sleep problems. The proposed approach is expected to be a reference in the development of a more accurate sleep disorder diagnostic aid system.
A Framework for E-Government Service Management Implementation in Indonesia: An Actor-Network Theory (ANT) Perspective
The Electronic Government Service Management System (EGSMS) is mandated by the Indonesian government; however, its implementation remains limited, particularly at the local government level. This study aims to examine the implementation of EGSMS in Madura, Indonesia, using Actor-Network Theory (ANT) as the theoretical lens to understand interactions among human actors, technological artefacts, institutional arrangements, and regulatory instruments. The research was conducted in the Departments of Communication and Informatics in Pamekasan and Sampang, Madura, Indonesia, during 2023 and 2024. Based on two local government case studies, this study develops an ANT-based framework for EGSMS implementation, which was subsequently reviewed by two experts to strengthen its relevance, validity, and practical applicability. The findings reveal actor-network dynamics, implementation challenges, enabling factors, and best practices in local EGSMS adoption. The novelty of this research lies in applying ANT to construct a context-sensitive EGSMS implementation framework grounded in empirical evidence. This framework is significant because it can serve as a practical guide for implementing and improving EGSMS in other local government contexts in Indonesia.
Comparative Performance Analysis of YOLOv12 and RF-DETR in Face Detection
Face detection in dense and occluded environments remains a significant challenge in computer vision. This study compares the CNN-based YOLOv12 and the Transformer-based RF-DETR to determine the optimal balance between accuracy and latency for resource-constrained edge computing. Using the WIDER FACE dataset and an NVIDIA T4 GPU, multiple model variants were evaluated. Due to GPU memory constraints during training of the RF-DETR Medium variant, a standardized batch size of 8 was implemented across all models. To ensure methodological rigor, quantitative metrics (precision, recall, F1-score, mAP) were strictly assessed on the validation set. Concurrently, a 100-image subset of the test set was used exclusively for inference efficiency benchmarking, completely separate from detection evaluation. Results indicate YOLOv12X achieved superior overall detection performance (F1-score: 0.764, mAP@50:95: 0.440), significantly outperforming RF-DETR Medium. For real-time applications, YOLOv12M demonstrated the highest efficiency (36.17 FPS vs. 23.32 FPS). Qualitatively, YOLOv12 maintained high sensitivity in crowded scenes, whereas RF-DETR provided stable small-scale face detection despite its lower recall. Overall, under these constrained-hardware conditions, YOLOv12 appears to be a highly viable solution for surveillance systems, while RF-DETR offers a stable alternative for small-object detection when computational overhead and training budgets are less restrictive.
Detecting Experience Debt in Internal IT Services: A Minimal Touchpoint-Based XLA for Small EdTech Startups
Small EdTech startups often use SLA numbers to check whether internal IT services are working well. The problem is that these numbers usually show only whether the service was restored and how long it took. They do not show what users actually experienced while the problem was happening. This study looks at that hidden part. It uses the idea of experience debt to describe small but repeated service problems that slowly make daily work harder. The study was conducted in three small EdTech startups in Jakarta. It used survey data and interview data collected in the same period. The survey produced 274 valid responses across six touchpoints and five experience dimensions. The interviews involved 18 informants and focused on the service moments they remembered as most difficult. The weakest point appeared at TP2, where users faced access problems and often felt confused about what was happening, what to do next, and whether the issue was really finished. At TP4 and TP5, the main problem was fairness. Users often felt stuck in a process they could not see, had to repeat the same explanation, or did not know who was handling their request. From these findings, the study developed a simple touchpoint-based XLA as a one-page review tool. Its purpose is to help small teams notice user experience problems that normal SLA monitoring often misses. More research is still needed in other service settings and organisations.
Mapping the Global Landscape of Electronic Supply Chain Management (e-SCM): A Bibliometric and Visual Analysis
This study maps the intellectual structure of global electronic supply chain management (e-SCM) research through a bibliometric analysis of Scopus-indexed publications published between 2015 and 2025. The retrieval workflow began with the Scopus query TITLE-ABS-KEY ("supply chain management") and was followed by structured interface-based refinement using pub- lication period, subject area, and document type constraints to construct the final analytical corpus. Bibliometric performance indicators were analyzed using the Bibliometrix R- package, while science mapping and network visualization were conducted using VOSviewer. The findings show that the e-SCM literature is organized around five major thematic concen- trations: sustainability in supply chain management, environmental and circular-economy integration, operational decision support and risk analytics, sectoral and stakeholder coordi- nation, and the recent acceleration of blockchain, Industry 4.0, and digital transformation. Co-authorship and country-level mappings indicate a multicentric global research structure led by China, India, and the United Kingdom, while temporal overlay visualization shows a marked shift toward digitally enabled governance and resilience-oriented research during 2022–2023. These results provide an evidence-based synthesis of the evolution of the field and a replicable bibliometric foundation for future sector-specific studies in sustainability- sensitive supply networks.
Citizen Role Representation in Digital Government Maturity Models: A Systematic Review
This study investigates how citizen roles are represented and structurally positioned within Digital Government Maturity Models (DGMMs). Using a Systematic Literature Review guided by Kitchenham’s protocol, the review analyzed studies published between 2020 and 2025 across seven major academic databases. From 900 initial records, 14 studies met the inclusion and quality criteria. Through backward tracing, policy reference analysis, and cross-model extraction, these studies produced 77 DGMMs as the final units of analysis. The models were examined using a role-based analytical lens that classifies citizen representation into five levels: None, Limited, Implicit, Explicit, and Strong. The findings show that the Limited category remains dominant, indicating that most DGMMs still position citizens mainly as end-users or service recipients rather than active participants in digital governance processes. However, the increasing presence of Explicit and Strong models reflects a gradual shift toward participatory, collaborative, and citizen-centric digital governance. This study contributes by proposing a typology of citizen role representation that extends prior descriptive mappings into a deeper structural evaluation of how citizen participation, engagement, and co-creation are embedded within digital maturity
Logistic Regression Modeling of Peatland Fire Hotspots in Bengkalis District Using Integrated Environmental and Anthropogenic Drivers
Peatland fires occur almost annually in Bengkalis District, Riau Province, Indonesia, where peatlands cover about 65% of the area and contribute significantly to carbon emissions and regional haze, highlighting the need for improved fire risk prediction. This research aims to apply a probabilistic logistic regression approach to predict peatland fire hotspot occurrence and identify its key drivers. Hotspot data from 2015–2023 were derived from VIIRS satellite observations and classified into low (l), nominal (n), and high (h) confidence levels. Then hotspot confidence levels are classified into two scenarios: (1) the nh scenario (l = 0; n–h = 1) and (2) the h scenario (l–n = 0; h = 1), representing different fire thresholds. The predictor variable was modeled using anthropogenic and environmental, with multicollinearity testing to ensure model stability. The results show that the nh scenario performs better, with Nagelkerke R² = 0.0681, Hosmer–Lemeshow χ² = 5.7663, AUC = 0.69, and accuracy = 95.19%, indicating acceptable fit and moderate discrimination. Significant predictors include plantation land use, peat characteristics, and precipitation. These findings suggest that the approach can support peatland fire risk assessment, although further refinement is required.
Optimizing Stroke Prediction Using Backward Elimination and SMOTE with C4.5 and K-Nearest Neighbors
Early prediction of stroke risk is crucial for reducing mortality and the burden on the healthcare system, but class imbalance and irrelevant features often compromise model reliability. This study analyzes the impact of Backward Elimination and SMOTE on the performance of the C4.5 and K-NN algorithms in stroke prediction. The study used a fixed working subset of 1,239 data points and evaluated four modeling scenarios using Stratified 10-Fold Cross Validation. Model performance was measured using accuracy, precision, recall, F1-score, and AUC. The results showed that Backward Elimination improved model performance on the analyzed subsets. For C4.5, accuracy increased from 70.94% to 73.05%, stroke recall from 83.94% to 85.14%, and AUC from 0.776 to 0.806. For K-NN, accuracy increased from 72.31% to 74.82% and precision from 39.91% to 42.73%, while stroke recall remained relatively stable at 74.30%. These findings indicate that although the improvements are small numerically, the results remain practically relevant as they enhance the balance between sensitivity and class discrimination capability. In the context of stroke screening, reducing false negatives is more important because it helps minimize undetected high-risk cases, although false positives still need to be considered as a consequence of further testing. Overall, C4.5 with Backward Elimination demonstrates more balanced performance, although the results are still limited to the analyzed subset.
A Hybrid Feature-Enriched IndoBERT Framework for Sentiment Analysis of Ride-Hailing Service Reviews in Indonesia
This study examines sentiment classification for Indonesian ride-hailing user reviews, which often contain informal expressions, ambiguity, and strong contextual dependency. Existing studies commonly rely on either traditional machine learning or transformer-based models, while limited attention has been given to integrating heterogeneous feature representations. To address this gap, this study proposes a feature-level hybrid integration strategy combining TF-IDF and IndoBERT embeddings. This approach enables the model to capture statistical term importance and contextual semantic meaning within a unified representation. A quantitative experimental design was applied to approximately 20,000 reviews collected from Gojek, Grab, and Maxim. Sentiment labels were generated through rating-based mapping and manually validated for consistency. The dataset, which was relatively balanced across positive, neutral, and negative classes, was divided into training and testing sets using an 80:20 split. Model performance was evaluated on the test set using accuracy, precision, recall, and F1-score. The proposed hybrid model achieved the highest accuracy of 93.5%, outperforming IndoBERT (91.8%) and traditional machine learning models (78.4%–87.6%). The results show that feature-level integration improves sentiment classification performance, although neutral sentiment remains challenging due to contextual ambiguity.
Climate Change Impacts, Agro-Ecosystem Stress, and Adaptive Agricultural Strategies in South Asia: A Scopus Bibliometric Analysis (1997–2026)
This study presents a bibliometric analysis of research on climate change impacts, agro-ecosystem stress, and adaptive agriculture in South Asia from 1997 to 2026. Initially, 2,260 documents were retrieved mainly from the Scopus database, and 1,580 documents were selected after applying predefined screening criteria. Using bibliometric and informatics-based analytical tools, the study examines publication trends, collaboration networks, influential contributors, and thematic evolution in the field. The findings reveal a significant increase in scholarly output, particularly after 2015, indicating growing regional and global concern over climate-induced agricultural challenges. Keyword analysis highlights the dominance of climate change, drought, and resilience, reflecting a strong focus on ecological vulnerability, food security, and livelihood sustainability. Country-level analysis shows that India leads research production, followed by Pakistan, Bangladesh, and China. Major thematic clusters include climate resilience, sustainable agriculture, food security, and environmental stress management. Despite this growth, gaps remain in the integration of information systems, interdisciplinary collaboration, and region-specific adaptation strategies. This study provides a structured overview of the intellectual landscape and offers useful insights for researchers, policymakers, and practitioners working toward climate-resilient and sustainable agriculture in South Asia.