Journal of Information Systems and Informatics
Journal of Information System and Informatics
Asosiasi Doktor Sistem Informasi Indonesia
Authors Declaration The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
Sentiment Analysis of User Reviews for AI Applications: Evaluating SVM, Logistic Regression, and Random Forest
The rapid growth of AI applications such as CICI, GROK, and Gemini has resulted in a large volume of user reviews on platforms like the Google Play Store, making sentiment analysis a critical tool for understanding user perceptions. This study compares the performance of three machine learning models: Random Forest, Support Vector Machine (SVM), and Logistic Regression in classifying sentiments in 3,500 Indonesian-language reviews. A hybrid feature extraction approach, combining sentiment lexicons with TF-IDF, was applied to improve sentiment classification accuracy. The models were evaluated based on accuracy, precision, recall, and F1-score. Results indicated that all models achieved an accuracy greater than 96%, with Random Forest providing the most consistent and accurate results, achieving an overall accuracy of 99.62%. While SVM excelled in classifying positive and negative sentiments, it faced challenges with neutral reviews due to the ambiguity and overlap in sentiment expression. Logistic Regression also showed strong performance, especially on structured reviews. The findings suggest that Random Forest is the most robust and reliable model for sentiment analysis, particularly in handling diverse AI application reviews. These results offer practical insights for developers seeking to improve application performance by leveraging sentiment analysis on user feedback.
sentiment analysis, Random Forest, Support Vector Machine, AI applications, machine learning
Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
Implementation of the Collaborative Filtering Method for a Clothing Sales Recommendation System in Fashion Store
The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.
Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X
Towards Self-Defending SDN Infrastructures: Real-Time Honeypot-Enabled Botnet Detection Using ONOS
Modern Software-Defined Networks (SDNs), while benefiting from centralized programmability, remain vulnerable to fast-evolving botnet attacks. This paper presents and evaluates a lightweight ONOS-based honeypot and decoy framework designed to detect and automatically block multi-vector botnet behaviors in real time. The system integrates honeypot-exposed Telnet, SMB, and DNS services with threshold-, entropy-, signature-, and correlation-based inspection within a tree topology (depth = 2, fanout = 4) consisting of five OpenFlow switches and 50 hosts. Quantitatively, the system achieved 100% detection of all signature-based attacks (55/55), 100% blocking of distributed UDP scans (50/50), and 0% false positives on benign decoy access. Median detection latency ranged between 1–3 seconds. True positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) were measured using ground-truth attacker lists built into automated test scripts, yielding precision and recall of 1.00 across all malicious scenarios. This work demonstrates that combining deception with SDN-level flow automation enables effective and computationally efficient botnet defense without machine learning. A key limitation is that all evaluations were conducted exclusively in a controlled Mininet simulation, which may not fully represent real-world traffic dynamics. Future work will validate the system on physical SDN deployments and evaluate its robustness under production workloads.
Bibliometrics Analysis of Bankruptcy Prediction Trends in MSMEs: Global Insights from (2020–2025)
The purpose of this study is to map the development of research on bankruptcy prediction in Micro, Small, and Medium Enterprises (MSMEs) during 2020–2025 and to identify major scientific trends, influential authors, and dominant methodological approaches. Using a bibliometric method, data were collected from the Scopus database, producing 144 initial documents that were filtered into 23 final publications based on relevance and open-access availability. Performance analysis and science mapping were carried out using VOSviewer through co-authorship, co-citation, and keyword co-occurrence networks. The findings reveal four main research clusters: (1) financial-ratio-based distress models, (2) machine-learning approaches for SME risk prediction, (3) post-pandemic MSME resilience, and (4) credit scoring using non-financial indicators. Scientometrics is identified as the most influential journal, while Edward I. Altman and Alessandro Giannozzi emerge as central scholars. The United States, Italy, and the United Kingdom appear as the most collaborative and productive countries. The novelty of this research lies in its specific focus on MSME bankruptcy prediction during the post-pandemic era, the use of an open-access-filtered dataset, and the identification of emerging thematic clusters. However, this review is limited to Scopus-indexed, English-language, and open-access publications, which may exclude relevant studies from other sources.
Quantum Computing in Molecular Design and Drug Discovery: A Systematic Literature Review
This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.
Navigating Digital Careers: A Multi-Case Study of Women’s Career Decisions in Indonesia’s IT Sector
Indonesia’s rapid digital transformation has intensified demand for IT talent, yet female attrition remains high. Aligning with the Sustainable Development Goals’ emphasis on inclusivity, this study examines women’s IT career decisions in Indonesia through the Individual Differences Theory of Gender and IT (IDTGIT). Using a qualitative multi-case design, 12 semi-structured interviews with female IT professionals reveal three career trajectories: stayers (women who remain in IT roles), movers (transitioning to non-IT sectors), and leavers (exiting the workforce completely). Findings show that career decisions are shaped by the interaction between internal drivers (self-actualization, personal characteristics, and career–person fit) and external contexts (organizational culture, relational support, and societal infrastructure). We found that work–family conflict and value reorientation emerge as pivotal mediators triggering transitions across career paths. This study advances IDTGIT by demonstrating its applicability in a developing, collectivist country and introducing a comparative framework across three career decisions. Practically, the findings suggest operationalizing flexible work arrangements through a Results-Only Work Environment (ROWE) and asynchronous tools, while strengthening inclusive policies via gender-responsive health support and accessible childcare to accommodate women’s dual professional and caregiving roles.
Evaluating Pinterest User Experience and Usability Using AttrakDiff and PLS-SEM
The rapid development of visual platforms such as Pinterest necessitates a comprehensive understanding of how functional and emotional aspects jointly influence users’ perception and engagement. This research addresses the gap in user experience (UX) evaluation of visually rich applications by examining the effects of Pragmatic Quality, Hedonic Quality-Stimulation, and Hedonic Quality Identity on the perceived Attractiveness of the Pinterest application. A quantitative approach was employed using the 28-item AttrakDiff instrument, based on data collected from a final sample of 524 valid respondents, predominantly aged 18–25 years, and using Pinterest several times a week. The data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the aid of SmartPLS to examine the relationships among latent variables. The findings demonstrate that the structural model exhibits a high level of explanatory capability, with an R² value of 0.684. With all three UX dimensions exerting positive and statistically significant effects on Attractiveness. PQ shows the strongest influence (path coefficient = 0.457), followed by HQS (0.391) and HQI (0.112). These findings confirm that functional usability remains the primary driver of attractiveness on Pinterest, while hedonic qualities play a complementary role in enhancing user experience. Practically, this research suggests that designers and developers of visual platforms should prioritize efficient functionality while maintaining stimulating and identity-supporting elements to improve overall user appeal.
Sentiment Analysis of Indonesian Netizens toward Vasectomy on X Using the IndoBERT Model
Vasectomy-related conversations on X (Twitter) frequently generate polarized pro–contra debates that can shape public understanding of male contraception, yet evidence on Indonesian netizens’ sentiment remains limited. This study maps and classifies sentiment toward vasectomy during April–June 2025 using a descriptive quantitative text-mining and NLP pipeline. After preprocessing (cleaning and deduplication), 9,817 posts were analyzed. Semi-supervised labeling was performed using the teacher model taufiqdp/indonesian-sentiment with confidence-based refinement, supported by a rule-based sarcasm_flag that identified 330 potentially sarcastic texts. A 20% manually verified GOLD subset (1,963 samples) served as ground truth, and IndoBERT (indolem/indobert-base-uncased) was fine-tuned with weighted cross-entropy and early stopping. Evaluation on the GOLD test set (n = 393) showed strong performance (accuracy = 0.8168; macro F1 = 0.8141), with most errors concentrated in short, ambiguous, or humor/sarcasm-leaning posts. Full-corpus predictions produced 3,957 negative, 3,520 positive, and 2,340 neutral texts, indicating a contested and polarized discourse with a slightly higher negative share. These findings support the need for evidence-based digital communication strategies to address misconceptions and stigma surrounding male contraception.
Student Performance Classification Using Academic, Socioeconomic, and Digital Behavior Features: A Comparative Study
Accurate prediction of student academic performance is essential for universities seeking to improve learning outcomes and deliver timely, data-driven support. Prior work commonly uses regression to estimate Grade Point Average (GPA), yet numeric predictions can be difficult for administrators to translate into actionable risk levels. This study reframes the task as binary classification, categorizing students as good (GPA ≥ 3.00) or poor (GPA < 3.00) performers. Using 2,423 records from multiple programs at an Indonesian university, we combine academic indicators from the learning management system (login frequency, assignment submission, and forum activity) with socio-economic and digital behavioral variables (parental income, extracurricular participation, study-group involvement, and social media use). Seven machine learning models—Naïve Bayes, Generalized Linear Model, Logistic Regression, Deep Learning, Decision Tree, Random Forest, and Gradient Boosted Trees (GBT)—are benchmarked under a consistent evaluation design. Results indicate that integrating academic, socio-economic, and digital behavioral features improves classification performance, and ensemble methods outperform single, traditional models. GBT yields the best accuracy of 0.75, offering a practical basis for early-warning dashboards and targeted interventions. The study provides comparative evidence from Indonesian higher education and highlights the value of incorporating digital engagement signals alongside conventional academic data for more effective student support services.
The Role of E-Participation in Building Public Trust and Readiness for E-Voting: A Systematic Review
Digital Electronic voting (e-voting) success relies heavily on institutional trust and public readiness, yet existing literature predominantly emphasizes technical specifications over socio-political factors. This study addresses this gap by systematically synthesizing how e-participation mechanisms contribute to public trust and readiness for e-voting adoption. A systematic Literature review (SLR) following the PRISMA 2020 framework was conducted, analyzing 40 peer-reviewed journal articles published between 2020 and 2025 from reputable databases. The finding reveal that readiness is influenced by interrelated factors, primarily trust in e-voting systems (n=9), technology (n=7), and government (n=6), alongside digital literacy (n=6) and ICT access (n=5). Crucially, the analysis identifies that e-participation mechanisms specifically online consultations and feedback loops serve as foundational learning tools that foster the necessary social readiness and mitigate resistance toward new electoral technologies. The Study contributes a conceptual reframing of e-participation as a prerequisite mechanism for building digital trust, rather than just a democratic feature. These findings provide guidance for policymakers designing inclusive and trustworthy digital electoral systems.
Determinants of Impulsive Buying During Shopee Flash Sales: Ajzen’s Theory of Planned Behavior Approach
This research investigates the psychological elements that affect consumers’ impulsive buying behavior during Shopee flash sale events using the TPB. This inquiry employs a quantitative causal approach using survey data from 154 Shopee users engaged in flash sale purchases. Data were analyzed using a variance-based structural equation modeling approach with SmartPLS. The findings indicate that AT, SN, and PB jointly demonstrate significant effects on impulsive buying intention (β = 0.401; β = 0.395; β = 0.161), jointly explaining 59.9% of its variance. In addition, impulsive buying intention demonstrates a strong influence on actual impulsive buying behavior (β = 0.656, p < 0.001), accounting for 43.1% of the behavioral variance. Among the antecedents, attitude represents the most dominant predictor of intention, followed by subjective norms. A key advancement of this research stems from the integration of the TPB framework within flash sale contexts, positioning impulsive buying intention as a central psychological mechanism under conditions of time pressure. from a practical standpoint, the findings suggest that Shopee sellers and digital marketers should emphasize benefit-oriented messaging, urgency cues, and social validation features such as reviews, real time purchase indicators, and influencer endorsements to strengthen consumers’ impulsive buying intention during flash sale campaigns.
IT Governance Audit Using COBIT 5: A Case Study of Banyumas Regency Regional Library
Penelitian ini bertujuan untuk mengevaluasi tingkat implementasi tata kelola Teknologi Informasi di Perpustakaan Daerah Kabupaten Banyumas menggunakan kerangka kerja COBIT 5. Penelitian ini dilakukan untuk mengatasi beberapa isu yang diidentifikasi terkait dengan responsivitas layanan TI, manajemen kontinuitas, tata kelola risiko, dan optimasi sumber daya, yang sangat penting untuk memastikan layanan perpustakaan yang andal dan berkelanjutan. Pendekatan studi kasus deskriptif digunakan, yang melibatkan pengumpulan data melalui tinjauan pustaka, observasi langsung, wawancara terstruktur, dan kuesioner yang dibagikan kepada 16 responden yang terdiri dari kepala perpustakaan, pustakawan, dan staf TI. Penilaian difokuskan pada empat proses COBIT 5, yaitu DSS02 (Manajemen Permintaan Layanan dan Insiden), DSS04 (Manajemen Kontinuitas), EDM03 (Optimasi Risiko Terjamin), dan EDM04 (Optimasi Sumber Daya Terjamin). Berdasarkan hasil penelitian, tingkat kemampuan rata-rata yang diperoleh dari semua domain penilaian adalah 4,06, menunjukkan proses yang dapat diprediksi di mana praktik tata kelola TI telah didefinisikan dengan baik, diukur, dan diimplementasikan secara konsisten. Skor kemampuan tertinggi dicapai pada DSS02 (4,24), sedangkan DSS04 mencatat skor terendah (3,95), yang menyoroti kelemahan dalam pemulihan bencana dan manajemen keberlanjutan bisnis. Keunikan studi ini terletak pada penerapan pengukuran tingkat kemampuan COBIT 5 dalam konteks perpustakaan umum, yang memberikan bukti empiris dan rekomendasi praktis untuk meningkatkan kematangan tata kelola TI dan mendukung penyediaan layanan publik yang berkelanjutan dan berorientasi pada nilai.
A Systematic Review of Agentic AI for Threat Detection and Mitigation in 5G Networks
Fifth-generation (5G) networks face escalating security challenges driven by decentralised architectures, stringent ultra-low-latency requirements, and rapidly evolving threat landscapes. Agentic Artificial Intelligence (agentic AI) autonomous systems that perceive network conditions, decide on countermeasures, and act in real time offers a promising route toward adaptive defence. This systematic review examines how agentic AI is being applied to detect and mitigate threats within 5G networks. Following PRISMA 2009 guidelines, four databases (IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect) were searched, yielding 22 eligible peer-reviewed studies published between 2020 and 2025, selected for explicit 5G relevance and empirical evaluation. The reviewed evidence clusters into four primary security areas: anomaly detection, DDoS mitigation, network slicing security, and intrusion detection. Across these domains, approaches based on federated learning, deep reinforcement learning, and multi-agent systems generally report stronger detection performance and/or more adaptive response behaviour than conventional, reactive baselines, while supporting privacy-preserving intelligence at the edge. However, key deployment barriers remain: 86% of studies rely on simulation-based validation, scalability beyond 100 nodes is insufficiently characterised, and reported coordination delays (120–180 ms) may conflict with 5G latency constraints in time-critical settings. To consolidate findings, this review proposes a Perception–Decision–Action–Feedback conceptual framework and highlights priorities for real-world validation and deployment-oriented evaluation.
Analysis of Enabling and Inhibiting of Cloud Storage Restriction Policy Implementation in Higher Education: A Systematic Literature Review
This study examines enabling and inhibiting factors affecting the implementation of cloud storage restriction policies (e.g., quotas, retention/archiving, data classification, and exceptions) in higher education institutions. A PRISMA-guided systematic literature review was conducted using Scopus and complementary manual searches in Google Scholar, covering English-language publications from the last ten years. Following de-duplication and staged screening, 30 studies were included for synthesis. Findings were analyzed using the Socio-Technical Systems (STS) framework and mapped across People, Structure, Technology, and Process dimensions to capture the interplay between policy design, implementation practices, and technical enforcement. Results indicate that implementation success is driven by human readiness and governance capacity, especially continuous training and mentoring, clear communication, and leadership support that helps balance cost control with user acceptance and compliance. Technological enablers include adequate infrastructure, platform/service integration, and strong access control and information security mechanisms to ensure consistent enforcement. Key barriers include limited digital and data literacy, resistance to change and concerns about data deletion, uneven IT capacity across units, weak SOPs, siloed coordination, complex bureaucracy, vendor dependence, and budget constraints. The study recommends a holistic approach combining strengthened governance, standardized processes, targeted technology investment, and structured change management.
Factors Influencing Generative AI Adoption for Knowledge Management in South Africa’s Automotive Sector
South Africa’s automotive sector is under increasing pressure to sustain competitiveness amid Fourth Industrial Revolution (4IR) transitions, persistent operational inefficiencies, and workforce ageing. Generative AI (GenAI) presents a potential pathway to strengthen knowledge management (KM) by supporting faster knowledge capture, synthesis, retrieval, and decision support. This study identifies the determinants of GenAI adoption for improving KM practices in South Africa’s automotive context. A quantitative, hypothesis-driven design was employed, integrating constructs from the PPOA, TEOG, and IEO frameworks to provide a consolidated adoption perspective. Survey data were collected from 142 industry participants and analysed using SPSS (correlation and multiple regression). The model demonstrated strong explanatory power (Adjusted R² = 0.624, p < 0.001). Results indicate that GenAI adoption is significantly and positively influenced by FATAA ethical principles, KM practices, GenAI tool capability, perceived enjoyment, perceived usefulness, compatibility, competition intensity, organisational size, mimetic pressure, and normative pressure (p < 0.05). In contrast, perceived ease of use and coercive pressure were not statistically significant in this context (p > 0.05). The study contributes a context-specific, integrated adoption model for GenAI-enabled KM in an under-researched setting and offers actionable implications for managers and policymakers focused on responsible, effective GenAI deployment.
A Regulation-Based Readiness Assessment Model for Smart City Development in Indonesia
This study addresses the lack of a smart city readiness assessment instrument that is explicitly aligned with Indonesia’s urban governance framework, particularly Government Regulation No. 59 of 2022. Existing readiness models often provide generic or technology-centred measures and do not sufficiently operationalise national regulatory requirements, limiting their utility for Indonesian local governments. To fill this gap, the study develops a regulation-based smart city readiness model comprising measurable, context-specific indicators that support readiness evaluation prior to implementation. The research adopts a Design Science Research (DSR) methodology, supported by a PRISMA-guided Systematic Literature Review to identify and synthesise candidate indicators, followed by iterative refinement. Instrument validation was conducted through expert judgement, face validity, and inter-rater reliability testing using Cohen’s Kappa. The final output is a validated readiness assessment instrument consisting of 70 indicators organised into five regulation-derived dimensions: infrastructure, facilities, public utilities, human resources, and suprastructure. Reliability results show strong inter-rater agreement (κ = 0.895), indicating robust and consistent indicator classification. The study contributes a policy-aligned readiness instrument grounded in Indonesia’s regulatory context and provides local governments with a standardised tool to assess readiness, identify development gaps, and support evidence-based planning for sustainable smart city implementation.
Enhancing Recruitment Transparency Using Simple Additive Weighting in Smart City Governance
The advancement of digital governance requires municipal recruitment processes that are transparent, accountable, and based on measurable criteria. In many local government environments, recruitment remains manual or semi-structured, increasing subjectivity, reducing efficiency, and limiting the traceability of decision outcomes. Although Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method are widely applied for candidate ranking, prior work often emphasizes technical scoring accuracy with limited attention to Smart City governance needs such as transparency, auditability, and accountable decision justification. This study develops and evaluates a SAW-based DSS to support objective, transparent, and traceable recruitment decisions within a Smart Governance context. Using a quantitative system development approach, candidate attributes were transformed into numerical scores and assessed through weighted criteria: education, work experience duration, English proficiency, age (cost criterion), and relevance of work experience. The SAW computation produced consistent and interpretable rankings, with the highest preference score reaching 98.462, indicating reduced reliance on unstructured subjective judgment. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.6 (“Excellent”), demonstrating strong acceptance and practical feasibility across stakeholder roles. Overall, the proposed system functions as a governance-support tool that strengthens transparency and accountability in public-sector recruitment.
Simple Additive Weighting, Decision Support System, Smart City, Workforce Recruitment
Stacking Ensemble Learning for University Student Dropout Prediction
Student dropout in STEM programs remains a persistent challenge for higher education institutions, reducing educational quality, weakening retention outcomes, and increasing inefficiencies in resource utilization. This study develops an interpretable Stacking Ensemble Learning approach to predict STEM student dropout risk and identify key academic and socioeconomic determinants that can support data-driven early intervention. Following the CRISP-DM framework, we analyze 3,630 student records from the UCI Machine Learning Repository containing demographic, academic, and socioeconomic attributes. The proposed stacking architecture combines Random Forest, Gradient Boosting, and XGBoost as base learners with Logistic Regression as a meta-learner, while SMOTE–Tomek Links is employed to address class imbalance and reduce boundary noise. Experimental results show that the model achieves strong predictive performance with 90.91% accuracy and ROC–AUC of 95.72%, demonstrating stable discrimination and outperforming individual base models. Feature importance analysis indicates that early academic trajectory variables—especially first- and second-semester success rates, total approved units, and average grades—are the most influential predictors of dropout risk. The proposed framework contributes a practical, interpretable early warning model by integrating stacking ensemble learning with imbalance handling and trajectory-based feature engineering, supporting actionable intervention planning in higher education.
Design and Implementation of a Hierarchically Interoperable Tag-Based File System using FUSE (PreTFS)
Traditional hierarchical file systems make semantic organization awkward: a file that naturally belongs to multiple contexts must be forced into a single directory, leaving users to choose an arbitrary location or rely on duplication, linking, or search. This paper presents the design, prototype, and evaluation of a file system that preserves conventional hierarchical standards while adding an opt-in, tag-based semantic layer for multi-context categorization. We describe (i) a design in which tags are represented as directories with reserved, prefixed names and tag intersections are expressed through ordinary path nesting, and (ii) a proof-of-concept implementation that validates feasibility in practice. The implementation, PreTFS, is built as a FUSE (Filesystem in User Space) file system and uses SQLite to store file metadata and content. Results show that the design is realizable and remains compatible with conventional applications and workflows without external tools or specialized APIs. Benchmarking against a native kernel file system (btrfs) reveals expected overheads from user-space indirection and metadata management, measuring approximately ~2–73 ms for metadata-oriented operations and ~1–160 ms for file-content operations. These costs indicate the approach is practical for small-scale environments such as personal information management, where semantic flexibility and interoperability can outweigh peak performance. The novelty lies in a simple, hierarchically interoperable tagging design that enables semantic categorization through standard directory navigation.
Trinity-Controller ADWIN: An Accuracy Guided Sensitivity Control Framework for Streaming Intrusion Detection
Concept drift can severely undermine the reliability of streaming Intrusion Detection Systems (IDS), especially in realistic network traffic where changes are gradual, recurring, and often masked by noise and class imbalance. Widely used statistical drift detectors such as ADWIN provide theoretical guarantees, yet in practice they can exhibit sensitivity oscillations, delayed adaptation under subtle drift, and disruptive reset behavior that leads to prolonged performance dips. This paper presents Trinity-Controller ADWIN, a unified drift-management framework that fuses three complementary signals: a Volatility Controller (VC) for statistically grounded drift detection, an Adaptive Rate Controller (ARC) that dynamically regulates ADWIN sensitivity, and a Performance-Based Controller (PBC) that monitors an Exponential Moving Average (EMA) of online accuracy to detect sustained model degradation. The proposed framework is evaluated using a Hoeffding Adaptive Tree classifier on a time-ordered streaming reconstruction of CICIDS2017, reflecting realistic temporal drift patterns. Across multiple drift regions, Trinity-Controller ADWIN achieves higher long-horizon accuracy stability, faster post-drift recovery, and fewer unnecessary resets than fixed ADWIN, VC-only, and VC+ARC baselines. Notably, in several drift segments the framework preserves post-drift accuracy above 90% of baseline while demonstrating near-zero recovery delay, indicating that adaptation occurs with minimal disruption. Overall, the results show that combining statistical drift evidence with direct performance-aware feedback yields a more robust and operationally reliable streaming IDS under evolving traffic conditions.
Event-Based Detection of Provocative Political Discourse on Indonesian Twitter: A Comparative Study of SVM and IndoBERT
Political polarization on Indonesian social media intensified during the August 2025 House of Representatives (DPR) demonstrations, where provocative and sarcastic tweets helped amplify institutional criticism and widen public conflict. This study examines event-based automatic detection of provocative political discourse by comparing a feature-based Support Vector Machine (SVM) classifier with a transformer-based IndoBERT model on a large-scale Indonesian Twitter (X) corpus collected from 15 August to 15 September 2025. Tweets were preprocessed and labeled using a rule-based proxy lexicon to distinguish provocative from neutral content, then both models were trained and evaluated under the same experimental setting. Results show that SVM is highly effective for recognizing explicit provocation expressed through repetitive and lexically salient slogans, whereas IndoBERT provides more stable detection of implicit and context-dependent provocation, including irony and sarcasm that are common in Indonesian political talk online. In addition, temporal exploration indicates sharp spikes in tweet volume that align with key offline protest moments, suggesting a close coupling between street-level mobilization and digital discourse dynamics. Overall, the findings support the use of contextual NLP models within event-centered social media analysis to strengthen scalable monitoring of polarization and to inform early-warning approaches for escalating conflict in Indonesia’s digital public sphere.
A Web-Based Disaster Report Recapitulation System Using the Simple Additive Weighting Method
Many regional disaster management agencies still manage incident reporting through conventional, semi-manual workflows (e.g., spreadsheets and paper archives). These practices often create repetitive recapitulation tasks, increase the likelihood of data inconsistency, and produce reports that remain largely descriptive—limiting analytical support for timely, evidence-based decision-making. To address this limitation, this study develops a web-based disaster reporting and recapitulation system integrated with the Simple Additive Weighting (SAW) method to generate an event-level Impact Index for prioritization. The system is built using a Prototyping approach, enabling iterative refinement through user feedback to ensure operational fit. SAW is applied using weighted criteria—number of casualties, affected families, damage level, and disaster type—so that each recorded event can be scored and ranked automatically. In contrast to many prior disaster-related SAW applications that emphasize beneficiary selection or aid distribution, this research applies SAW for internal managerial evaluation, prioritizing disaster events themselves to support organizational review and mitigation planning. A case study at BPBD Minahasa Regency demonstrates the system’s feasibility and performance: Black Box Testing achieved a 100% functional success rate, and manual SAW verification confirmed that automated Impact Index outputs are mathematically consistent with theoretical calculations. Overall, the proposed application offers a structured and transparent analytical tool to standardize reporting, accelerate recapitulation, and strengthen decision support through objective impact-based ranking.
From Catch to Consumer: A Process-Centric Model for Resilient Fisheries Value Chains
Capture fisheries value chains are highly fragmented and time-sensitive, with multiple stakeholders and perishable products creating persistent challenges for quality management, traceability, and resilience. Many improvement initiatives prioritize digital technologies, yet benefits often remain limited when tools are not embedded in day-to-day business processes. This study proposes a process-centric framework that integrates Porter’s Value Chain with Business Process Model and Notation (BPMN) to diagnose and redesign capture fisheries workflows. Using a case-based approach, As-Is BPMN models are developed across key actors—fishing vessels, landing/auction sites, processors, and distributors—to identify process gaps, information discontinuities, and subjective decision points that weaken end-to-end traceability and quality assurance. Building on these insights, a To-Be process architecture is designed that embeds standardized identifiers, explicit decision logic, quality checkpoints, and traceability controls directly into operational workflows. The findings indicate that process-oriented redesign strengthens information continuity, accountability, and compliance readiness, shifting traceability from a retrospective reporting obligation to an operational management mechanism. Methodologically, the study demonstrates how Porter’s Value Chain can function as a process landscape and how BPMN connects strategic value creation to execution. Practically, the framework offers actionable guidance to improve governance and resilience in capture fisheries value chains.
An ADDIE-Based Educational Arithmetic Game for Children with Mild Intellectual Disabilities
This study presents the design and evaluation of BRILIAN, an educational arithmetic game developed to support children with mild intellectual disabilities using the ADDIE instructional design framework. The study addresses persistent challenges in numeracy learning-particularly addition and subtraction-where conventional instructional approaches often fail to sustain learner engagement and instructional effectiveness. A mixed-methods approach was employed across the ADDIE phases, incorporating teacher interviews, functional testing, standardized usability and user experience instruments (System Usability Scale, User Experience Questionnaire-Short, and Game User Experience Satisfaction Scale), classroom observations, and pre-test/post-test performance assessments. The findings indicate high usability and user satisfaction, with a mean SUS score of 75.20 and an overall GUESS score of 87.74%. Teacher observations further revealed strong learner engagement (90.26%). Learning effectiveness improved significantly, as reflected by an increase in mean test scores from 62.73 to 82.27, accompanied by reduced task completion time. By integrating structured instructional design, multimodal feedback, adaptive gameplay, and cognitive accessibility features, this study provides empirical evidence that ADDIE-based educational games can function as practical and effective instructional tools for arithmetic learning in special education classroom contexts.
Spatio-Temporal Graph Neural Network for Solar Irradiance Prediction: A Case Study in Nganjuk, Indonesia
Solar energy utilization in tropical regions is strongly influenced by the accuracy of solar irradiance estimation, which is affected by temporal variability and spatial atmospheric interactions. Conventional forecasting approaches commonly rely on single-station time-series models, limiting their ability to capture regional dependencies. This study proposes a spatio-temporal modeling framework based on Graph Neural Networks (GNN) to estimate solar irradiance by explicitly incorporating spatial relationships among observation sites. The study focuses on Sawahan Subdistrict, Nganjuk Regency, Indonesia, using solar irradiance data collected from five Automatic Weather Stations (AWS) operated by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) during 2024. Each station is represented as a graph node, with spatial connections constructed based on geographical distance, while temporal dependencies are modeled using Long Short-Term Memory (LSTM). Experimental results show that the proposed model achieves a Mean Absolute Error (MAE) of 102.64 W/m², a Root Mean Squared Error (RMSE) of 166.76 W/m², and an R² value of 0.6446 for the target location. These findings demonstrate that GNN-based spatial aggregation improves estimation stability and accuracy, providing practical support for localized solar energy assessment in tropical regions.
A Hybrid Machine Learning and Signature-Based Approach for Detecting Network Pivoting in BYOD Environments
This study addresses the challenge of detecting network pivoting, a lateral movement technique that is difficult to identify in insider and BYOD environments because malicious transitions can resemble normal internal activity. The objective was to improve detection of both known and unknown pivoting behaviours while supporting practical triage in resource-constrained institutions. A hybrid detection framework was developed that fuses Snort signature alerts with machine learning classification and unsupervised anomaly detection using behavioural features derived from BYOD-like network traffic. The approach was evaluated in a controlled testbed and supported by organisational survey findings on awareness and monitoring practice. Results show the hybrid system achieved 96.2% classification accuracy with a 4.5% false positive rate when distinguishing normal traffic, suspicious activity, and pivoting attacks. Compared with signature-only and machine-learning-only baselines, the hybrid design detected simulated pivoting attempts earlier and more consistently. User acceptance testing also reported strong satisfaction with the integrated dashboard for monitoring, filtering, and reporting. The key contribution is a unified, dashboard-oriented fusion of signature and behavioural evidence that strengthens early lateral movement detection and reduces manual correlation effort.
Small Language Models for Drug-Drug Interaction Extraction from Biomedical Text: A Systematic Literature Review
Drug–drug interaction (DDI) extraction from biomedical text is central to pharmacovigilance but remains challenging in resource-constrained clinical environments. While large language models have shown promise, their computational cost and deployment complexity limit practical adoption. This study systematically reviews the role of small language models (SLMs) for DDI extraction and examines their effectiveness, efficiency, and deployability. A systematic literature review was conducted following PRISMA guidelines, covering empirical studies published between 2020 and 2025 in PubMed, IEEE Xplore, ACM and SpringerLink. Eligible studies were analysed with respect to model architectures, datasets, evaluation metrics, and deployment considerations. Quality assessment was applied to ensure methodological robustness. The synthesis indicates that SLM-based approaches, including CNN-, LSTM-, and lightweight transformer models, can achieve competitive F1-scores on benchmark DDI datasets while requiring substantially fewer computational resources than large language models. However, performance varies across datasets, and real-world clinical evaluations remain limited. These findings support the feasibility of deploying SLM-based DDI extraction systems in resource-constrained clinical and pharmacovigilance settings and provide a baseline for future benchmarking and comparative research in clinical natural language processing.
A Decision Support System for Assessing High School Students' Soft Skills Using the Analytical Hierarchy Process
Assessing students' soft skills in educational settings is often challenging due to the subjectivity and inconsistency inherent in evaluating qualitative traits. This study employs the Analytical Hierarchy Process (AHP) as a decision support tool to provide a more systematic, consistent, and objective method for evaluating students' soft skills. The assessment model is based on four key criteria—critical thinking, communication, collaboration, and creativity—each further broken down into measurable subcriteria. The study was conducted at MA Mu’allimin Sruweng Kebumen, where evaluations were carried out by a guidance and counseling teacher acting as an expert evaluator, using a numerical scale ranging from 1 to 100. Pairwise comparison matrices were developed using Saaty’s fundamental scale to determine the weights for both criteria and subcriteria, followed by consistency testing using the Consistency Ratio (CR). The findings reveal that critical thinking and collaboration were assigned the highest priority weights, with all comparison matrices meeting the acceptable consistency threshold. The resulting global preference values offer a more objective, proportional representation of students’ soft skills achievements. This AHP-based model enables fairer, more consistent evaluations and provides quantitative outputs that can be utilized for student ranking and structured feedback in educational decision-making.
Implementation of a Web-Based Media Partnership Registration Information System Using Waterfall Model
The Minahasa Regency Department of Communication and Informatics manages public information and cooperation with press media organizations, yet press media partnership registration is still handled manually. This causes processing delays, data duplication, limited traceability, and poor visibility of verification status, increasing administrative workload and reducing service transparency for applicants. This study implements a web-based Press Media Partnership Registration Information System to digitalize and standardize the registration workflow. Development follows the Waterfall model, including requirement analysis, system design, implementation, testing, and refinement to ensure structured deliverables suitable for government environments. The system provides CRUD-based data management, administrator-led verification with status tracking, and automated email notifications for verification outcomes. Functional validation uses Black Box Testing to evaluate input-output behavior against predefined specifications. Test results show that core modules—account registration, login, partnership submission, verification (approve/reject with notes), CRUD operations, session control, and email notification—operate correctly and meet functional requirements. The implemented system is feasible for operational use and improves efficiency, data accuracy, traceability, and transparency in local government press media partnership administration.
Towards Cloud-Based Electronic Health Records in Healthcare Systems: Security, Scalability, and Migration Strategies: A Systematic Literature Review
Cloud-based Electronic Health Records (EHRs) are being adopted rapidly worldwide, but implementation still encounters recurring obstacles in security assurance, elastic scalability, and migration readiness. Prior reviews often treat these issues separately, leaving limited practical guidance for organizations planning end-to-end deployment. This study synthesizes recent evidence on cloud EHR adoption by examining how security controls, scalability claims, and migration strategies interact in real implementation contexts. A systematic literature review following PRISMA guidelines was conducted across ACM Digital Library, PubMed, IEEE Xplore, and ScienceDirect, covering peer-reviewed studies published from 2021 to 2025. Results show that the literature is technically mature in proposing encryption, access control, auditing, and performance optimization, and frequently reports scalability advantages. In contrast, evidence on complete migration pathways—data mapping, interoperability, validation, cutover planning, and post-migration assurance—remains sparse, with many studies relying on simulations rather than longitudinal deployments. The review also identifies geographic concentration in high-income settings, limiting generalizability to resource-constrained health systems. By integrating security, scalability, and migration readiness within a socio-technical, implementation-oriented perspective, this review provides actionable directions for secure and scalable cloud EHR transitions.
Multimodal Implicit Sentiment Analysis for Tourism Development: A Systematic Literature Review
This study aims to examine the application of multimodal approaches in implicit sentiment detection within the tourism sector to support data-driven digital development strategies. This review identifies prevailing trends, methodologies, datasets, and scientific novelties in multimodal sentiment analysis capable of capturing hidden emotions, such as sarcasm and ambiguity, in tourist reviews. Using a systematic literature review approach, ten core studies published between 2020 and 2025 were analyzed to identify prevailing research trends, dominant methodological frameworks, commonly used datasets, and emerging scientific contributions. Results demonstrate that multimodal deep learning models—particularly those employing attention-based fusion and contrastive learning—consistently outperform unimodal approaches in recognizing nuanced tourist emotions that are not explicitly stated in text. Despite these advances, the review reveals a significant gap in tourism-specific and Indonesian-context studies, as well as an overreliance on general-purpose social media datasets. This review provides a conceptual and methodological foundation for implementing multimodal implicit sentiment analysis in tourism decision-making systems, enabling destination managers and policymakers to develop early warning mechanisms for tourist dissatisfaction, enhance destination quality assessment, and support more targeted and sustainable tourism development strategies.
The Impact of Team Dynamics on Software Quality and Productivity: Evidence from South Africa
Software quality and productivity are influenced not only by technical practices but also by the social dynamics within development teams. This study investigates the combined effect of team dynamics, including trust, communication, collaboration, diversity, and conflict resolution, and software development practices on project outcomes. A mixed-methods design combined regression, Spearman’s Rho, and thematic analysis of survey data from 124 South African software professionals. The findings indicate that trust is the strongest positive predictor of software quality and productivity, while communication effectiveness and the use of collaboration tools also improve software outcomes. Equally, unstructured collaboration, excessive planning meetings, and poorly managed communication channels negatively affect performance. Diversity and effective conflict resolution were positively associated with productivity and efficiency. Thematic analysis corroborated these findings, illustrating how unclear communication, low trust, and dysfunctional collaboration lead to delays, rework, and lower quality. The study confirms that successful software outcomes emerge from the alignment of social and technical subsystems, highlighting the critical role of team dynamics in realising the full potential of software development practices. It contributes empirical evidence from an understudied developing-country context and proposes a socio-technical framework to enhance software quality and productivity.
Application of the Key Performance Indicator Method in an Employee Information System
The rapid development of information technology has significantly encouraged the integration of information systems in human resource management to enhance efficiency, effectiveness, and objectivity. However, performance appraisal systems that lack standardized indicators can lead to subjectivity and inconsistency, impacting employee productivity and managerial decision-making. This study proposes a web-based Personnel Management Information System (PMIS) that integrates Key Performance Indicators (KPIs) to provide an objective and measurable performance evaluation system. The system design incorporates KPIs, weights, and targets, supported by a structured, transparent process for performance assessments. The system was implemented at PT Kebon Agung Trangkil, a sugar industry company, to improve employee performance evaluations and managerial decision-making. This research adopts the Waterfall system development method and includes a User Acceptance Test (UAT) with 15 respondents, achieving an 88% acceptance rate. The results indicate that the developed system improves assessment efficiency, reduces subjectivity, and supports more transparent decision-making. The study concludes with recommendations for expanding the system’s capabilities and improving KPI validation through formal methods.
Centroid Optimization of K-Means Using Ant Colony Optimization for Culinary MSME Clustering
Micro, Small, and Medium Enterprises (MSMEs) are economic activities conducted by individuals or groups, particularly in the culinary sector. The rapid expansion of culinary MSMEs, especially in tourism-oriented regions such as the Special Region of Yogyakarta, necessitates effective data clustering to systematically analyze their characteristics. High-quality clustering plays a crucial role in supporting informed decision-making, including business development planning, MSME assistance programs, and the formulation of well-targeted policies. This study applies the K-Means algorithm to cluster culinary MSME data; however, its performance is sensitive to centroid initialization, which may result in suboptimal clustering outcomes. To address this limitation, Ant Colony Optimization (ACO) is employed as a centroid optimization approach. ACO is a metaheuristic algorithm inspired by the foraging behavior of ant colonies, where pheromone trails guide the search toward optimal solutions. The results indicate that the integration of ACO enhances clustering performance compared to K-Means. The silhouette scores obtained are 0.88 and 0.89 for two clusters, 0.80 and 0.86 for three clusters, and 0.80 and 0.92 for four clusters for K-Means and ACO-optimized K-Means, respectively. These findings demonstrate that ACO effectively improves centroid initialization, with four clusters identified as the optimal configuration.
Modeling EMIS Adoption with PLS-SEM: Integrating the Government Adoption Model and DeLone–McLean IS Success Model
This study explores the key factors influencing the adoption of the Education Management Information System (EMIS) within Indonesia's Ministry of Religious Affairs (Kemenag), which is vital for managing data and distributing Teacher Professional Allowances (TPG). Data inconsistencies have been a significant challenge, leading to delays in TPG disbursement. To understand the determinants of EMIS adoption, this study integrates the Government Adoption Model (GAM) and DeLone & McLean’s (D&M) Information Systems Success Model. A quantitative approach was used, collecting data from 328 valid responses from MTsN teachers in Kebumen Regency, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Perceived Uncertainty (PU), Perceived Security (PSC), and Perceived Privacy (PP) positively contribute to Perceived Trust (PT). Additionally, Information Quality (IQ) emerged as the strongest predictor of EMIS adoption, followed by System Quality (SYQ), Service Responsiveness (PSR), and Trust. The study emphasizes that improving data accuracy (IQ), ensuring system reliability (SYQ), and strengthening security measures (PSC) are critical for accelerating EMIS adoption. The findings offer practical implications for Kemenag to optimize the implementation of EMIS, ultimately improving the efficiency and timeliness of TPG disbursements for educators.
Cyberbullying Detection in Indonesian TikTok Comments Using IndoBERT with Fairness Evaluation
This study investigates automated cyberbullying detection on TikTok within the Indonesian digital context, where high social media usage among children and adolescents demands scalable and consistent content moderation. We propose an IndoBERT-based framework for detecting and classifying cyberbullying in Indonesian-language TikTok comments, incorporating algorithmic fairness considerations. A dataset of 2,122 TikTok comments was collected from a publicly available Kaggle repository and divided into training, validation, and testing sets using a 70:15:15 stratified sampling ratio. The IndoBERT-base-p1 model was fine-tuned with the PyTorch and HuggingFace frameworks, optimizing hyperparameters like the AdamW optimizer and learning rate scheduling. Experimental results show that the model achieved an accuracy of 70.66% and a ROC-AUC score of 0.7969, demonstrating solid discriminative power. With a macro F1-score of 0.7066 and a cyberbullying recall of 0.7170, the model shows balanced performance in identifying harmful content. A key contribution of this study is a fairness evaluation framework that reveals an accuracy gap of 2.08% and an equal opportunity gap of 0.0208, indicating overall fairness. However, demographic parity remains a concern. This system, supporting content triage combined with human review, enhances moderation workflows by filtering non-cyberbullying cases while flagging potentially harmful content for human oversight.
Feature Selection vs Dimensionality Reduction for Steam Game Metadata Classification: An Ensemble Learning Study
Optimizing noisy Steam game metadata is essential for accurate binary classification. This study compares feature selection (MI) and dimensionality reduction (PCA, LDA) using a dataset of 55,144 Steam reviews and four ensemble algorithms, evaluated through Stratified 5-Fold Cross-Validation. The results show that the 125-feature baseline achieved the highest accuracy of 0.7728 with CatBoost. Feature selection (FS_10) maintained competitive performance with an accuracy of 0.7449, while LDA, after optimization, achieved 0.7281. In contrast, PCA significantly hindered performance (0.6963) due to the inability of linear transformations to preserve the discriminative power of one-hot encoded categorical features, which ensemble models handle better in their original form. These findings highlight the importance of preserving original features, especially in low-to-medium dimensional metadata, where feature selection outperforms dimensionality reduction in maintaining predictive integrity. High accuracy is crucial for developers to track product reception and for platforms to improve recommendation systems that influence user purchasing decisions. The study concludes that for Steam game metadata, strategic feature selection is superior to dimensionality reduction for maintaining classification performance.
Integrating SERVQUAL and ECM to Explain E-Wallet Satisfaction and Continuance in Semi-Urban and Rural Indonesia
This study aims to analyze user satisfaction and continuance intention toward mobile payment applications in semi-urban and rural Indonesia by integrating the SERVQUAL framework and the Expectation–Confirmation Model (ECM). Although e-wallet adoption has increased rapidly, empirical evidence on post-adoption behavior in non-urban contexts remains limited. This study addresses this gap by examining how service quality, expectation, confirmation, and digital literacy shape satisfaction and continued usage. Using a quantitative survey, data were collected from 212 active DANA users in Central Lombok Regency during December 2025–January 2026 and analyzed using PLS-SEM with SmartPLS. The results show that among the five SERVQUAL dimensions, only empathy has a significant positive effect on user satisfaction. Expectation and confirmation significantly influence satisfaction, whereas perceived usefulness does not directly affect either satisfaction or continuance intention. User satisfaction and digital literacy significantly predict continuance intention, while the moderating effect of digital literacy is not supported. From a practical perspective, these findings indicate that providers should prioritize user assistance, communication, and expectation management rather than interface or technical attributes when serving semi-urban and rural markets. This study demonstrates that expectation fulfillment and relational-based service quality are more decisive for sustaining e-wallet usage than technical features in non-urban settings.
Performance Comparison of Random Forest, XGBoost, and SVM for Flood Risk Prediction Using BNPB GIS Data
This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—for predicting flood risk using spatial and non-spatial data from BNPB GIS. The analysis focuses on disaster records from January 3 to 15, 2026, with district-city as the spatial unit of observation. Following data cleaning, exploratory analysis, and feature preparation, the models were evaluated using ROC-AUC, PR-AUC, F1-Score, Precision, Recall, and Accuracy. XGBoost demonstrated the highest ROC-AUC (0.675), indicating strong overall performance in distinguishing flood from non-flood events. Random Forest achieved the highest Recall (0.947), showing superior sensitivity in detecting flood events, while SVM exhibited fluctuating performance with a lower ROC-AUC (0.496). Visualizations of model behavior and spatial flood patterns were provided to support model interpretability. The study’s results suggest that ensemble models, particularly XGBoost and Random Forest, can significantly enhance flood risk prediction, improving the accuracy and sensitivity of early warning systems. These findings contribute to the development of more effective data-driven flood mitigation strategies in Indonesia, enabling better disaster preparedness and response.
AI and Digital Transformation Trends: A Systematic Review with Multi-Criteria Analysis
This research investigates the integration of Artificial Intelligence (AI) and Digital Transformation (DT) as critical enablers of Industry 4.0, highlighting their combined influence in reshaping industrial processes and enhancing operational efficiency. AI technologies, including machine learning, natural language processing, and computer vision, are driving advancements in automation, real-time decision-making, and personalized services across various industries, such as manufacturing, healthcare, and logistics. DT involves the widespread adoption of digital technologies that transform business models, stakeholder interactions, and organizational structures, working synergistically with AI to foster innovation. While existing literature often examines AI and DT in isolation, this study addresses the gap by employing a Systematic Literature Review (SLR) and Multi-Criteria Analysis (MCA) methodology to evaluate research based on academic impact, practical relevance, and sectoral readiness. The analysis reveals emerging trends such as predictive analytics, autonomous systems, and smart manufacturing, with industries like healthcare and retail showing strong adoption, while real estate and legal services remain underexplored. The research examines 46,936 Scopus records and selects 32 studies for analysis. The MCA results underscore the importance of aligning academic research with industrial needs and fostering cross-sector collaboration. Ultimately, this study bridges the gap between theory and practice, offering valuable insights for policymakers, scholars, and practitioners to strengthen competitive advantage in the digital era.
A Decision Support Model for Online Lending Creditworthiness Using Comparative Personality Indicators
The rise of online lending platforms has improved financial accessibility but also increased credit default risk due to information asymmetry and limited borrower profiling. Traditional creditworthiness models rely primarily on financial and demographic data, which often fail to capture behavioral characteristics. This study proposes a decision support model for creditworthiness prediction by integrating personality indicators from the Big Five Personality Traits and the California Psychological Inventory (CPI). The framework incorporates these personality-based features into a machine learning-driven system alongside traditional borrower data. Psychological indicators are quantified and assessed using multiple classification models to evaluate their impact on predictive performance. The model's effectiveness is measured using metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Empirical results show a significant improvement in prediction accuracy, with the AUC rising from 0.74 in the baseline model to 0.87 after including personality features. A comparative analysis reveals the relative contributions of each personality framework, demonstrating that personality indicators enhance predictive performance over traditional models. These findings emphasize the value of incorporating behavioral factors, supporting the development of more effective and sustainable credit risk assessment systems.
A Layered Digital Investigation Framework for Internet of Things (IoT) Forensics: A Smart Home Camera Case Study
The rapid adoption of Internet of Things (IoT) technology in various sectors, such as smart homes, healthcare, and transportation, has provided significant efficiency. Nevertheless, many IoT devices are developed without a serious review of security standards, and forensic readiness consideration. As a result, these IoT devices are vulnerable to cyber-attacks that can potentially lead to malware attacks, and system manipulation. This study aims to propose and validate a digital forensic investigation framework in the IoT ecosystem. The framework layers designed in this study consist of the device layer, network layer, and cloud layer. Validation is carried out through a simulated crime scenario recorded by the Mi 360° smart home camera. Meanwhile, the analysis phase focuses on data source artifacts from the device layer, video metadata, technical attributes, and cryptographic integrity verification using hash values (MD5 and SHA-1) documented in the Chain of Custody (CoC) method. The experimental results of this study indicate that digital evidence artifacts sourced from across layers have reliable temporal and structural consistency in reconstructing the chronology of events. This framework successfully correlated artifacts across three layers to reconstruct a complete event timeline, demonstrating its practical validity in distributed IoT forensic investigation.
Usability Evaluation of a School Library OPAC Using Heuristic Evaluation and User Testing
This study evaluates the usability of the Online Public Access Catalog (OPAC) at SMK Negeri 1 Purwokerto to address the persistent gap between traditional library information architectures and the modern search behaviors of vocational students within the Kurikulum Merdeka ecosystem. The research aims to solve the problem of "mental model dissonance" that hinders independent information literacy among digital native learners. A hybrid evaluation approach was employed, integrating a Heuristic Evaluation by three experts with empirical User Testing involving students. The study utilized the Think-Aloud protocol and the System Usability Scale (SUS) to capture both performance and perception data. Result: The expert inspection identified 18 significant usability violations, primarily in library technical jargon (H2) and error prevention (H5). Empirical testing revealed a low average Task Success Rate (TSR) of 49.3% and a mean SUS score of 55.0, placing the system in the "Unacceptable" category. These figures confirm that current cataloging logic significantly obstructs retrieval efficiency. The originality of this research lies in the identification of specific dissonance points between vocational students' mental models and bibliographic metadata. It provides a strategic framework for interface restructuring through semantic simplification and department-based navigation, offering a practical model for developing user-centric "smart" library services in vocational education.
Analyzing the Impact of Review Sentiment on Carpentry Product Sales: Evidence from Tokopedia
The rapid growth of e-commerce in Indonesia has increased the importance of consumer reviews as signals influencing purchasing decisions. This study examines the relationship between review sentiment and sales performance in the carpentry tools category on Tokopedia. Using a 2019 Kaggle dataset consisting of 1,826 reviews across approximately 60 products, we apply an NLP-based pipeline to classify review sentiment into positive, neutral, and negative categories. Sentiment labeling combines rating-based rules and a TF-IDF + Logistic Regression baseline, with additional evaluation using IndoBERT. Product-level metrics—including the proportion of positive sentiment (pos_share), average rating, and units_sold (sales proxy)—are analyzed using descriptive statistics, correlation analysis, and cross-sectional OLS regression. The findings reveal that, in this snapshot dataset, the association between positive sentiment share and log(units_sold + 1) is weak and statistically limited, suggesting that sales variation cannot be explained solely by sentiment polarity or average ratings without considering other commercial factors. These results highlight the importance of incorporating contextual variables and temporal design in future research. Practically, the study suggests that sellers should monitor not only sentiment polarity but also the informational richness of reviews to strengthen reputation management strategies.
Post-Quantum Cryptography for securing Electronic Health Records in the South African Public Healthcare System
The growing dependency on Electronic Health Records (EHRs) has intensified the exposure of sensitive patient information to advanced cybersecurity threats, including those posed by quantum computing technologies. South African public hospitals depend on conventional encryption mechanisms to secure EHRs; however, these methods are susceptible to quantum threats. The study explored quantum-resistant cryptography for securing EHRs in South African healthcare. The study adopted a phenomenological approach, employing semi-structured interviews with 12 ICT specialists, policymakers, health information managers and cybersecurity practitioners. The study established a misalignment between national digital health and cybersecurity strategies and future quantum threats, as they prioritise digital transformation, data security and interoperability. Public hospitals were found to be reliant on conventional encryption methods, resulting in structural lock-in and impeding adaptability of post-quantum cryptography. Although stakeholders demonstrated awareness of quantum threats, organisational readiness remains constrained by technical, institutional and capacity barriers. It is concluded that South African public healthcare system remains behind towards post-quantum security transformation. The study recommends the development of a roadmap for post-quantum cryptographic migration, system modernisations and capacity building to strengthen the security of EHRs. The findings provide evidence-based guidance for policymakers to strengthen digital health security and resilience of EHRs in public healthcare systems.
Integrating EUCS and TAM to Evaluate DAPODIK User Satisfaction and Use in Central Lombok
This study evaluates user satisfaction and post-adoption behavior toward the Data Pokok Pendidikan (DAPODIK) Information System by integrating the End-User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) within a mandatory public education system context. Although DAPODIK has been nationally enforced and widely implemented in Indonesia, empirical evidence explaining how system quality translates into user satisfaction, technology acceptance, and actual use remains limited, particularly at the local government level. A quantitative survey-based approach was employed involving 255 active DAPODIK users in Central Lombok Regency, Indonesia, collected between December 2025 and January 2026 using a five-point Likert-scale questionnaire. Data were analyzed using PLS-SEM, encompassing measurement model evaluation, structural model assessment, hypothesis testing, and moderation analysis of digital literacy. The findings indicate that format, ease of use, and timeliness significantly influence user satisfaction, whereas content and accuracy do not show significant effects. User satisfaction has a strong positive effect on perceived usefulness and perceived ease of use, while behavioral intention significantly predicts actual system use. The moderation analysis further reveals that digital literacy strengthens the relationship between technology acceptance and users’ attitudes toward system use. From a practical perspective, the results suggest that policymakers should prioritize interface format, usability simplification, and system responsiveness during data collection cycles to enhance user satisfaction and sustained system use.
Sentiment Analysis of the Free Nutritious Meal Program on Twitter Using Naive Bayes and IndoBERT-Based Labeling
The Free Nutritious Meal Program is a government initiative aimed at improving the nutritional status of primary school children in Indonesia. However, its implementation has generated diverse public reactions on the X/Twitter platform, making systematic sentiment analysis essential for policy evaluation. This study analyzes public sentiment using two labeling approaches—translation-based TextBlob and IndoBERT contextual labeling—combined with Naïve Bayes and Linear SVC classifiers. A total of 2,903 Indonesian-language tweets were collected, preprocessed, and classified to compare the performance impact of each labeling method. The evaluation was conducted using accuracy, precision, recall, and macro F1-score. Sentiment distribution under IndoBERT indicates a predominance of negative and neutral opinions, particularly related to budget concerns, implementation quality, and food distribution issues. This study is subject to several limitations. The dataset size (2,903 tweets) and restricted temporal window may limit the generalizability of findings to long-term public discourse. The analysis also relies on a single social media platform (X/Twitter), excluding perspectives from other platforms such as Instagram or TikTok. Moreover, although IndoBERT improves contextual understanding, transformer-based labeling still may not fully capture sarcasm or highly colloquial expressions. Despite these limitations, the study demonstrates the effectiveness of combining Indonesian transformer models with conventional classifiers to support data-driven policy evaluation.
Evaluating Medxa SIMRS Implementation Success Using HOT-Fit and Multiple Regression
The implementation of Hospital Management Information Systems (HMIS) is essential for improving service quality, strengthening operational efficiency, and supporting evidence-based decision-making in hospitals. Nevertheless, the success of HMIS implementation is shaped not only by technological performance, but also by human and organizational factors. This study aimed to evaluate the implementation success of Medxa SIMRS at Mohammad Hoesin Hospital, Palembang, using the Human–Organization–Technology Fit (HOT-Fit) model. A quantitative cross-sectional survey was conducted among 67 active users of Medxa SIMRS using a structured Likert-scale questionnaire. Data were analyzed using descriptive statistics and multiple linear regression in Python to examine the influence of the Human, Organization, and Technology dimensions on Net Benefit. The descriptive findings showed that all HOT-Fit dimensions were rated in the good to very good categories, indicating generally positive user perceptions of the system. Regression analysis demonstrated that, simultaneously, the Human, Organization, and Technology dimensions significantly explained variation in Net Benefit (p < 0.05). However, in the partial analysis, only the Technology dimension had a statistically significant positive effect on Net Benefit. These results indicate that system quality, information quality, and service quality are the main determinants of perceived system benefits in this setting. The findings suggest that hospitals should prioritize technological optimization while strengthening organizational support and user readiness to maximize the success of HMIS implementation.
URL-Based Phishing Detection Using a BERT-LSTM Model
The rising prevalence of phishing websites presents substantial cybersecurity threats by deceiving users into revealing sensitive information through malicious URLs. This study aims to enhance phishing URL detection by introducing a deep learning model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM). In this framework, BERT is fine-tuned on a phishing URL dataset and utilized as a contextual embedding to represent URL tokens, while Bayesian Optimization is employed to identify optimal hyperparameter settings during model training. Experimental results demonstrate that the BERT-LSTM model achieves impressive detection performance, with a precision of 0.9299, recall of 0.9795, F1-score of 0.9540, accuracy of 0.9756, and ROC-AUC of 0.9962. The model consistently outperforms embedding-based methods such as Word2Vec, FastText, and GloVe, as well as a classical baseline model using Logistic Regression with TF-IDF features. These findings suggest that the contextual embeddings generated by BERT effectively capture structural patterns in URLs, leading to more accurate phishing detection and providing a promising approach for enhancing cybersecurity systems.