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Virtual vs Human Influencers: A Comparative Analysis Of Campaign Effectiveness Using Deepfake Detection Technology From The Perspective Of Ethics And State Responsibility Under The Rule Of Law
Purpose: This study aims to examine the effectiveness of AI-driven influencer marketing, develop an Xception-based Convolutional Neural Network (CNN) for deepfake detection, and analyze ethical and legal responsibility for artificial intelligence use from a rule-of-law perspective. Methods: A mixed-methods approach was employed by integrating Computer Science, Management, and Law perspectives. Quantitative analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among AI Personalization, AI Interaction, User Experience, and Trust. The Xception-based CNN was evaluated using standard classification metrics. Qualitative analysis involved expert interviews and examination of legal principles concerning AI governance, transparency, accountability, and consumer protection. Result: The Xception-based CNN achieved 97.12% accuracy and an AUC of 0.9920 in distinguishing authentic from AI-generated content. PLS-SEM results indicate that AI Interaction significantly influences User Experience and Trust, while User Experience has the strongest effect on Trust (β = 0.520; p < 0.001). The model explains 53.3% of the variance in Trust, and User Experience significantly mediates the relationship between AI Interaction and Trust. The legal analysis highlights the need for stronger AI governance addressing transparency, disclosure of AI-generated content, consent, accountability, and consumer protection. Novelty: The novelty of this study lies in integrating deepfake detection, AI-driven influencer marketing, consumer trust analysis, and rule-of-law perspectives into a unified framework for responsible digital marketing. This integrated approach provides a multidisciplinary perspective for balancing technological innovation with ethical responsibility, legal accountability, and consumer protection.
Copyright (c) 2026 Scientific Journal of Informatics
Automation of Sprint Cost Estimation in Agile Projects Using a Random Forest-Based Decision Support System
Purpose: The manual construction of a Work Breakdown Structure (WBS) is inherently susceptible to human cognitive biases; hence, the objective of this research shifts toward Intelligent Automation, leveraging a Decision Support System (DSS) to replicate expert financial logic. Methods: The dataset comprises 30 validated sprint records from 23 internal projects; other WBS documents were excluded due to incomplete sprint details. Pre-processing involved completeness checks and One-Hot Encoding of team role compositions, without feature scaling. A Decision Tree Regressor, Random Forest Regressor, and Hybrid Model (Voting Regressor) were evaluated using 3-fold cross-validation based on MAPE, RMSE, and R² metrics. Result: The Random Forest Regressor demonstrated the best relative performance among the three models, achieving a MAPE of 23.55%, an RMSE of IDR 35.27 million, and an R² of -0.535. Across the 30 sprint predictions, the Random Forest model showed a mean deviation of 23.55% ± 31.69%, a median of 9.98%, and a range of 0.78%–125.33%, highlighting performance variation across sprints and supporting the model's application within a human-in-the-loop framework. Feature importance analysis identified `sprint_duration_days` as the dominant feature, followed by `role_programmer_jr` and `role_qa_sr`. Novelty: These findings demonstrate the system's practical contribution, particularly in streamlining budgeting bureaucracy by accelerating the process from days of manual preparation to just a few minutes through automated execution.
Improving Scrum Implementation Quality in Software Development through an Integrated Capability Maturity Assessment and Decision-Making Framework
Purpose: This study evaluates the quality of Scrum implementation in the New Corporate Website project at XYZ Bank and proposes process improvement recommendations using an integrated framework based on the Capability Maturity Model Integration (CMMI) 2.0. CMMI 2.0 was selected because it provides comprehensive Practice Areas for assessing organizational capability beyond Scrum-specific maturity models. Methods: A convergent parallel mixed-method case study was employed involving 14 AHP experts, 17 Scrum appraisal respondents, and 3 interview participants. Quantitative and qualitative data were collected through questionnaires, semi-structured interviews, and document observations. The Analytical Hierarchy Process (AHP) was used to prioritize organizational problems, the Weighted Scoring Model (WSM) mapped the prioritized problems to the most relevant CMMI 2.0 Practice Areas, and Scrum capability was evaluated using the Key Process Area (KPA) Rating and SCAMPI C appraisal approach. Results: Five Practice Areas were selected for appraisal. Verification and Validation (VV) was the only Practice Area that achieved Capability Level 1 with a KPA score of 96.16%, whereas Project Planning (PLAN), Process and Quality Assurance (PQA), Peer Review (PR), and Requirements Development and Management (RDM) remained at Capability Level 0. Based on the identified weaknesses, 11 improvement recommendations were formulated and organized into short-, medium-, and long-term implementation phases to support continuous software process improvement. Novelty: This study proposes an integrated framework that combines AHP, WSM, and CMMI 2.0 to systematically prioritize Practice Areas before capability assessment. The proposed framework provides a structured decision-making mechanism that links organizational problems with capability appraisal and offers practical guidance for improving Scrum implementation and supporting continuous software process improvement.
A Comparative Study of Deep Learning Architectures for Content-Based Image Retrieval on a Stone Texture Dataset
Purpose: This study addresses a persistent gap in geological content-based image retrieval (CBIR): the reliance of existing sequence-based and hybrid deep learning models on static, two-dimensional rock texture representations that they were not originally designed to handle. Methods: Four architectures were evaluated on a unified 9,853-image, ten-category Stone-2D dataset, partitioned into training and validation subsets at an 8:2 ratio (7,882/1,971 images), with FAISS employed for similarity search over 128-dimensional embeddings. Findings: On an identical held-out test set, all four architectures achieved comparable performance (82.4%–85.2% accuracy; macro F1 0.826–0.860), with a standard 2D CNN achieving the highest accuracy (85.2%) and the custom pseudo-3D MineralNet architecture achieving the highest macro F1 (0.860). No architecture dominated across both metrics. Novelty: These results indicate that, once preprocessing is aligned with the native dimensionality of the image data, architectural complexity provides limited additional benefit for static geological texture classification — challenging the common assumption that specialized or sequential architectures are necessary for this task. A working CBIR retrieval pipeline was validated with representative query examples, demonstrating semantically consistent nearest-neighbour retrieval across distinct rock textures.
Real-Time Multi-Class DoS Attack Detection on Proxmox VMs UsingLightGBM with MikroTik Integration
Purpose: The adoption of virtualization increases the dependence on service availability, making Denial of Service (DoS) attacks a serious threat, while rule-based detection is poorly adaptive to evolving attacks. Many previous studies also rely on outdated public datasets, are evaluated offline, rarely measure inference latency, and lack automatic mitigation. This study aims to build a real-time multi-class DoS detection system on Proxmox virtual machines using LightGBM integrated with MikroTik. Methods: A controlled testbed based on Proxmox and MikroTik was built to generate normal and attack traffic. The dataset was collected from the real infrastructure at a one-second granularity and labeled into six classes, namely the normal condition and five DoS attacks. LightGBM was proposed as the detection model, while XGBoost, Random Forest, Decision Tree, and SVM served as baselines, compared using a temporal holdout to prevent data leakage, with SMOTE applied only to the training data. Findings: LightGBM was selected as the best model with an accuracy of 96.22%, a macro F1-score of 96.25%, and an inference latency of 1.369 ms. The four flooding attacks were detected almost perfectly, whereas Slowloris was the hardest class because it resembles normal traffic. Its PR-AUC dropped to 0.9271, and the system performed automatic mitigation at a median latency of 56.2 ms. Originality: This study integrates lightweight real-time detection with automatic firewall-based mitigation in a closed loop on real infrastructure, emphasizing the balance between accuracy and efficiency rather than the highest accuracy alone. Future work can extend it to distributed (DDoS) attacks.
An Evaluation of the Implementation of Electronic Medical Records on Healthcare Service Performance Using SEM-PLS
Purpose: The study aims to explore the factors that determine the successful implementation of Electronic Medical Records in community health centers of Indonesia in which System Quality, Information Quality, and Perceived Ease of Use are proposed to influence User Satisfaction, and then the impact of User Satisfaction on Healthcare Service Performance is measured. Methods: A quantitative cross-sectional survey was conducted involving 180 healthcare professionals, namely physicians, nurses, administrative staff, and information system operators who routinely use EMR systems. The data were collected through a five-point Likert-scale questionnaire. The proposed model is the DeLone and McLean Information Systems Success Model and Technology Acceptance Model (TAM) integrated. Data analysis was performed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4. Result: The results show that System Quality, Information Quality, and Perceived Ease of Use significantly and positively affect User Satisfaction, which then significantly positively influences Healthcare Service Performance. The results further show that User Satisfaction mediates the relationship between technical system characteristics and organizational performance, highlighting the importance of system reliability, information quality, and usability for improved delivery of healthcare services. Novelty: This paper presents an SEM-PLS model that integrates the DeLone and McLean Information Systems Success Model with TAM in explaining healthcare service performance through User Satisfaction. The proposed framework not only offers a holistic approach to evaluate EMR implementation but also provides practical insight to enable the sustainability of digital health transformation in Indonesian community health centers.
Business Intelligence Dashboard for Outdoor Equipment Rental Bundling Recommendations Using FP-Growth
Purpose: This study aims to address the underutilization of transaction data in the outdoor equipment rental industry by developing an integrated Business Intelligence (BI) dashboard using the FP-Growth algorithm to generate product bundling recommendations and support data-driven decision-making. Methods: A quantitative approach based on the Knowledge Discovery in Databases (KDD) framework was employed using 596 rental transactions from Batas Outdoor Rental recorded between January and May 2026. The data were preprocessed and transformed into a binary matrix using TransactionEncoder. FP-Growth was applied with a minimum support of 2% (0.02), while association rules were generated using a minimum confidence of 30% (0.30) and validated with a lift threshold of 1.20. Results: A quantitative approach based on the Knowledge Discovery in Databases (KDD) framework was employed using 596 rental transactions from Batas Outdoor Rental recorded between January and May 2026. The data were preprocessed and transformed into a binary matrix using TransactionEncoder. FP-Growth was applied with a minimum support of 2% (0.02), while association rules were generated using a minimum confidence of 30% (0.30) and validated with a lift threshold of 1.20. Novelty: This study integrates FP-Growth-based transaction analysis with an interactive BI dashboard specifically for outdoor equipment rentals. Unlike previous studies focusing primarily on product combinations or promotional packages, the proposed approach provides an end-to-end decision-support framework connecting transaction analysis, automated bundling recommendations, and interactive visualization.
Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification
Purpose: To evaluate the impact of image preprocessing techniques, specifically contrast enhancement and noise reduction, on improving the CNN performance on Pap smear image classification for early cervical cancer detection. Methods: Three CNN architectures (ResNet34, DenseNet121, and MobileNet-V2) were trained and evaluated on the SIPaKMeD dataset. Two preprocessing techniques were applied: Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and Perona-Malik Diffusion (PMD) filter for noise reduction. Model performance was assessed using a confusion matrix. Results: Preprocessing improved classification performance across all models. CLAHE significantly increased the accuracy of ResNet34 from 76.73% to 84.16% and DenseNet121 from 83.17% to 84.16%, while also providing modest improvement for MobileNet-V2. In contrast, PMD filtering yielded limited improvement and, in some cases, slightly reduced model performance. Novelty: This study provides a systematic comparison of contrast enhancement and noise reduction techniques across multiple CNN architectures. This study demonstrates that contrast enhancement is more effective than noise reduction in improving CNN performance. The study provides new pipelines for improving cervical cancer classification.
A Quantitative Comparative Analysis of NASA-TLX Workload between Staff and Non-Staff Employees in a Pharmaceutical Manufacturing Organization: Implications for Knowledge Management
Purpose: In pharmaceutical production, personnel may be exposed to different workload situations, since their job functions are affected by strict operational and regulatory standards. Non-personnel workers and personnel workers have different duties which can affect their perception of workload and their participation in knowledge sharing activities. However, empirical evaluations of the workload characteristics of these employee cohorts by means of the NASA Task Load Index (NASA-TLX) are still scarce. This study explores the perceived workload attributes of staff and non-staff workers on six modified NASA-TLX scales. The implications of the identified trends for knowledge management strategies are discussed. Methods: The method used for this research was quantitative, comparative and cross-sectional. The study’s data were collected using a structured questionnaire. The sample selected for the study was 30 staff and non-staff members of a pharmaceutical manufacturing company using purposive sampling. The tool assessed six modified NASA-TLX workload factors; Mental Demand, Physical Demand, Temporal Demand, Performance, Effort and Frustration Level using a five-point Likert scale. Dimension total Pearson correlation analysis and Cronbach’s alpha reliability test were performed to assess the instrument with the same data set. Descriptive analyses of the workload characteristics of staff and non-staff personnel were conducted using group mean scores and mean differences. No assertions regarding statistical significance were provided. Result: Non-staff personnel had higher mean scores than staff personnel in Mental Demand, Physical Demand, Temporal Demand, Effort and Frustration Level. For workload, the largest mean differences were found in Temporal Demand and Effort. Non-staff personnel also reported a higher Performance score. The Performance items, however, are positively worded and imply that higher scores reflect a more positive view of work accomplishment and quality, not a greater workload. The patterns shown here indicate that role-specific knowledge management practices can support continued improvement, learning and knowledge sharing activities. Novelty: This study presents the analysis of staff and non-staff employees according to their role and a complete workload analysis in the context of a pharmaceutical manufacturing environment with the modified NASA-TLX framework. The results provide a pragmatic basis for the development of knowledge management strategies relevant to the operational tasks and workload characteristics of each category of employees.
An Ablation Study on Stacked LSTM and SMOTE for Government Policy Sentiment Classification
Purpose: We compare four deep learning configurations of Standard LSTM without SMOTE, Standard LSTM with SMOTE, Stacked LSTM without SMOTE, and Stacked LSTM with SMOTE used for multi-class sentiment classification of Indonesian public comments on the government’s Free Nutritious Meals Program (MBG) policy posted on Instagram. Most existing research on this topic relies on single-layer LSTM models that fail to address class imbalance and the layered, often sarcastic semantic structure commonly found in informal Indonesian social media writing at the same time. We test whether a Stacked LSTM architecture combined with SMOTE can simultaneously model sequential word dependencies and high-level semantic patterns, while correcting the bias that class imbalance tends to introduce. Methods: We collected 1,000 Instagram comments from the account @ferryirwandi using the Instaloader library and labeled them into three sentiment classes with TextBlob. Preprocessing was carried out through six steps: cleaning, case folding, slang normalization with a custom dictionary, stopword removal, tokenization, and stemming via Sastrawi after which TF-IDF was used for feature extraction. The data was split 80:20 into training and testing sets before applying any oversampling; SMOTE was applied only to the training data, so the test set remained intact and free of data leakage. Performance was measured using Accuracy, Macro-Precision, Macro-Recall, and Macro-F1 Score, obtained from a multiclass confusion matrix based on a One-vs-Rest scheme. Result: Scenario 3 (Stacked LSTM without SMOTE) produced the top result, reaching 95.00% Accuracy, Macro-Precision of 0.9276, Macro-Recall of 0.8752, and Macro-F1 Score of 0.8973 the best outcome among the four configurations. In contrast, Scenario 4 (Stacked LSTM with SMOTE) performed worse, with accuracy dropping to 92.50% and the F1-Score falling to 0.8178. We attribute this decline to feature space overlap: the synthetic minority samples generated by SMOTE landed too close to the dominant neutral class within the high-dimensional, sparse TF-IDF feature space. Novelty: Our findings provide empirical support for the idea that hierarchical representations built by stacked recurrent layers are well suited to capturing the complex context, sarcasm, and long-range semantic relationships found in Indonesian political discourse. We also propose the four-scenario ablation design as a reusable method for disentangling how much architectural depth and data balancing each contribute, both independently and jointly, when working with imbalanced Indonesian social media sentiment data. Practical Implications: For practitioners working with TF-IDF features on narrow-topic datasets, our findings suggest that Stacked LSTM without oversampling is the more reliable configuration for multi-class sentiment analysis of Indonesian government-policy discourse on social media.
A Hybrid Quantum-Classical Workflow for Early-Stage Molecular Discovery: A Workflow-Level Proof-of-Concept
Purpose: Classical computational approaches struggle to model molecular quantum interactions, making drug discovery costly and time-consuming. Quantum computing shows promise in molecular modelling, but much of the existing work focuses on isolated proof-of-concept tasks. These studies usually do not examine the full molecular discovery process. This study demonstrates the feasibility of a unified hybrid quantum–classical workflow for early-stage molecular discovery and examines how quantum-based molecular representations influence later optimisation and prediction. Methods/Study design/approach: The study developed and evaluated a five-stage hybrid quantum-classical workflow integrating query interpretation, molecular screening with quantum re-ranking, VQE-based energy estimation, property prediction and report generation. The system was implemented using Qiskit, Qiskit Nature, RDKit, and spaCy on the QM9 dataset using noiseless state-vector simulation. Result/Findings: The NLP component was evaluated across four tiers: 180 template-based queries, 160 independently written queries, 1,000 independently written queries, and 1,000 externally sourced BioASQ Task B questions. F1-scores were 89.9%, 82.1%, 73.0%, and 71.3%. Screening achieved constraint satisfaction rates of 90%-100%. Quantum-informed ranking differed substantially from cosine similarity and evaluated the utility of this through top-k enrichment and multi-property hit rates. MIFM achieved a mean VQE absolute error of 0.0156 Ha compared with 0.0218 Ha for ZZFeatureMap and 0.0248 Ha for cosine similarity. VQE and property-prediction evaluations, scalability experiments, and simulated NISQ tests provided broader evidence of workflow behaviour. Novelty/Originality/Value: This study presents a reproducible hybrid quantum–classical workflow for early-stage molecular discovery, extended with a chemistry-aware MolecularInteractionFeatureMap (MIFM). The study does not claim universal quantum advantage or physical quantum-hardware validation.
Adversarial Vulnerabilities in Cooperative Multi-Agent Reinforcement Learning for Distributed 5G Security
Purpose: This paper focuses on examining the robustness of a cooperative Multi-Agent Reinforcement Learning (MARL)-based Intrusion Detection System (IDS) for intrusion detection in decentralised 5G security settings. Even though MARL techniques have proven effective against dynamic threats in decentralized 5G networks, current research has not considered any adversarial scenarios at all. Methods: A cooperative MARL-based Intrusion Detection System was developed through the CRISP-DM approach. Radio Access Network (RAN), MEC, and Core agents were trained using Centralised Training with Decentralised Execution (CTDE) and Deep Q-Network (DQN) methods. The algorithm was tested on the NSL-KDD and UNSW-NB15 datasets against Fast Gradient Sign Method (FGSM) evasion attacks (ε = 0.05-0.30) and Byzantine poisoning attacks with 5%, 10%, and 20% compromised agents. Result: The model achieved 96.94% accuracy on NSL-KDD and 85.15% on UNSW-NB15 in clean scenarios. The FGSM attack at ε = 0.20 resulted in substantial performance deterioration, leading to accuracy drops of 50.14 and 45.26 percentage points, respectively, and a simultaneous increase in false positives. Byzantine poisoning produced smaller but persistent decreases in accuracy of 12.03 and 2.62 percentage points, respectively. Novelty: This study provides among the first empirical evaluations of adversarial fragility in cooperative MARL-based intrusion detection within distributed 5G-oriented security abstractions, demonstrating that cooperative intelligence alone does not guarantee adversarial robustness.
Gibbs-BERTopic for Topic Modeling of Short Indonesian Social Media Texts on Artificial Intelligence Issues
Purpose: This study aims to evaluate whether integrating Gibbs Sampling into BERTopic improves topic modeling of short Indonesian social media texts concerning Artificial Intelligence. Baseline BERTopic is used as the primary comparator, while LDA and Top2Vec are included as external baselines representing probabilistic and embedding-based topic-modeling approaches. Methods: A corpus of 9,585 public posts from platform X, collected from January 2024 to May 2025, was modeled using Latent Dirichlet Allocation (LDA), Top2Vec, Baseline BERTopic, and Gibbs-BERTopic. Model performance was assessed using topic coherence, topic diversity, topic uniqueness, number of topics, and document coverage. The number of outliers was compared specifically between Baseline BERTopic and Gibbs-BERTopic because LDA and Top2Vec do not use an equivalent HDBSCAN-based outlier mechanism. Result: Baseline BERTopic generated 58 topics and 4,674 outliers, with coherence, diversity, and uniqueness scores of 0.480, 0.460, and 0.380. Gibbs-BERTopic generated 15 topics without outliers and achieved the highest corresponding scores of 0.510, 0.880, and 0.870. LDA and Top2Vec produced lower scores, with 58 and 84 topics, respectively. These findings indicate that, within the corpus and configuration examined, Gibbs-BERTopic provided a more coherent and distinctive topic representation, broader document coverage, and a more compact topic structure. Novelty: This study extends the evaluation of Gibbs-BERTopic to short Indonesian social media texts and compares it with probabilistic, embedding-based, and BERTopic-based models. The results highlight the importance of evaluating topic models using multiple complementary metrics rather than relying on a single performance measure.
An Integrated Cybersecurity Governance Ecosystem for Healthcare: A Quality-Appraised Systematic Review of Governance, Risk, and Compliance Frameworks for AI, Cloud, and Connected Health Technologies
Purpose: The growing use of digital technologies in healthcare has significantly increased exposure to cybersecurity risks, creating a need for governance approaches that go beyond traditional control-based security models. This study reviews how Governance, Risk, and Compliance (GRC) frameworks are applied in healthcare cybersecurity, focusing on their effectiveness, implementation challenges, and integration into organizational governance. Methods: Using the PRISMA methodology, literature was collected from PubMed, IEEE Xplore, and ScienceDirect, with 20 studies included in the final analysis and individually quality-appraised against six criteria. Findings: The findings indicate a shift from traditional frameworks to more flexible, integrated governance approaches that account for emerging technologies, including artificial intelligence (AI), cloud computing, and interconnected healthcare systems. While GRC frameworks help improve governance structures, strengthen cybersecurity, and support regulatory compliance, their effectiveness is often limited by factors such as skills shortages, resource constraints, regulatory complexity, and legacy systems. The study also identifies key enablers of successful implementation, including leadership involvement, cross-departmental collaboration, and continuous monitoring. Novelty: Based on these findings, an integrated healthcare cybersecurity governance framework is proposed that aligns regulatory, organizational, and technological dimensions within a unified model and provides practical insights to strengthen resilience in modern healthcare systems. Unlike prior reviews that examine GRC frameworks in isolation, this study also compares quality-appraised evidence across frameworks to show which approaches work best in different organizational contexts, including those governing AI, cloud computing, and other connected health technologies. For example, control-catalog frameworks such as ISO 27001 and COBIT are best suited to larger, well-resourced organizations, whereas AI governance frameworks succeed only once regulatory uncertainty is resolved internally.
Leveraging Internet of Things and Artificial Intelligence in Smart Agriculture to Enhance Food Security and Sustainable Farming: A Systematic Review
Purpose: Achieving global food security while maintaining environmentally sustainable agricultural systems remains a critical challenge amid population growth, climate variability, and resource constraints. Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies that support data-driven agricultural practices. This study systematically examines the applications, opportunities, challenges, and adoption factors of AI-IoT integration in smart agriculture, with particular emphasis on its potential contributions to food security and sustainable farming. Methods: A systematic literature review (SLR) was conducted following the PRISMA 2020 guidelines. Publications were retrieved from Scopus, IEEE Xplore, and ScienceDirect covering the period 2020-2024. From an initial 431 records, 17 empirical studies met the inclusion criteria and were analysed using narrative and thematic synthesis. Result: The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring. These technologies enable real-time decision support, early disease detection, productivity improvement, and resource optimisation. However, challenges remain, including data limitations, infrastructure constraints, integration complexity, and high deployment costs. Novelty: The study proposes a layered smart agriculture framework linking technological infrastructure, application domains, adoption conditions, operational outcomes, and sustainability impacts. The findings highlight key factors necessary for successful implementation, including infrastructure readiness, affordability, technological reliability, and capacity development for farmers. Crucially, the review demonstrates that the field of AI-IoT smart agriculture is technically advanced but socio-technically incomplete: the evidence base is dominated by proof-of-concept studies from Asia, with no empirical representation from Africa or the Americas, creating what this study terms an AI-IoT agricultural equity gap that fundamentally limits the technology’s contribution to global food security. The five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes, offering a replicable analytical scaffold for future empirical and policy research in this domain.
Stacking Ensemble with Hybrid Balancing for Aquaculture Water Quality Prediction
Purpose: This study aims to improve the accuracy of water quality classification models by addressing class imbalance and data noise. Aquaculture water quality monitoring is essential to support sustainable aquaculture production and maintain aquatic organism health. As aquaculture systems become more intensive, accurate predictive models are needed for effective monitoring and decision-making. Therefore, this study proposes a stacking ensemble model optimized using SMOTEENN hybrid balancing to improve classification performance on aquaculture water quality datasets. Methods: The proposed approach combines SMOTEENN hybrid balancing with a meta stacking ensemble framework. First, Synthetic Minority Oversampling Technique (SMOTE) was applied to balance minority classes by generating synthetic samples. Next, Edited Nearest Neighbor (ENN) was used to remove noisy data from both original and synthetic datasets. After preprocessing, the classification process employed a meta stacking ensemble model consisting of Extra Trees (ET) and Random Forest (RF) as base learners, while Logistic Regression (LR) served as the meta learner. The model was evaluated using the Aquaculture Water Quality (AWQ) dataset. Result: Experimental results show that the proposed model achieves the highest performance under the SMOTEENN scenario, reaching an accuracy of 99.88%, outperforming SMOTE 99.20%, ENN 99.78%, and the original imbalanced data 99.18%. The results indicate that combining class balancing and noise reduction significantly improves classification performance. Novelty: This study presents a novel integration of SMOTEENN hybrid balancing and meta stacking ensemble learning, offering an effective solution for handling imbalanced and noisy environmental datasets in water quality classification
Adoption of Artificial Intelligence in Chatbot Recommendation Systems for Complex Customer Preferences: A Case Study of Shopee E-Commerce
Purpose: This study identifies and analyzes factors influencing AI chatbot recommendation system adoption and proposes optimization strategies based on user perceptions and organizational decisions. Methods: A qualitative case study was conducted with Shopee as the unit of analysis. Nine participants three internal Shopee personnel and six active users were interviewed and selected via purposive sampling. Thematic analysis followed three stages (open, axial, and selective coding) guided by the TOE–TAM framework. Trustworthiness was ensured through member checking, peer debriefing, and four criteria: credibility, transferability, dependability, and confirmability. Result: Shopee's AI chatbot delivers personalized recommendations but users frequently experience information overload from irrelevant results. Three TOE dimensions technology readiness, organizational readiness, and external pressure, were found to drive adoption, while four TAM factors perceived usefulness, ease of use, trust, and satisfaction, shape user acceptance. Five strategic recommendations are proposed: algorithm enhancement, data quality improvement, adaptive personalization, deeper customer profiling, and information overload reduction. Novelty: Prior studies examine organizational adoption (TOE) or user acceptance (TAM) of AI chatbots in isolation, leaving a gap in understanding how macro-level institutional readiness interacts with micro-level user cognitive barriers. This study addresses that gap by integrating TOE and TAM as a dual-perspective lens, explaining how institutional readiness spanning technology, organization, and environment directly reduces cognitive barriers during automated recommendations. The study further foregrounds the "Complex Customer Preferences vs. Information Overload" paradox as a central challenge: AI chatbots deployed to manage complex preferences often generate overload that undermines user trust and satisfaction, a tension prior TOE–TAM integrations have not addressed.
Copyright (c) 2024 Scientific Journal of Informatics
Comparison of Tree-Based Survival Models for Predicting Graduate Study Duration and Risk Stratification
Purpose: This study aims to develop and evaluate a tree-based survival machine learning framework for predicting graduate study duration and identifying students at risk of delayed graduation. The study addresses the growing need for accurate educational time-to-event prediction to support academic monitoring and data-driven decision-making in higher education institutions, including IPB University. Methods: A quantitative predictive analysis was conducted using data from 3,417 master students from the 2020–2022 cohorts. Four tree-based survival models were evaluated, namely Survival Tree (ST), Extremely Randomized Survival Tree (EST), Random Survival Forest (RSF), and Gradient Boosting Survival (GBS). The analysis used right-censored survival data with a 42-month observation period. Model evaluation was conducted using repeated stratified random split validation (10 repetitions) with Concordance Index (C-index) and Integrated Brier Score (IBS) metrics. Risk stratification was subsequently performed using the best-performing model based on predicted survival probabilities at a 24-month time horizon. Result: GBS achieved the best overall predictive performance with the highest mean C-index (0.659) and the lowest mean IBS (0.194), indicating superior discrimination and prediction accuracy compared to ST, EST, and RSF. The repeated evaluation results also demonstrated stable predictive performance across data partitions. Risk stratification successfully separated students into low-, medium-, and high-risk groups with significantly different survival patterns. High-risk students generally tended to be older, have lower undergraduate GPA, were more often male, and more frequently originate from private undergraduate institutions. Novelty: This study provides a comparative evaluation of multiple tree-based survival machine learning models within an educational time-to-event framework. The integration of repeated survival model evaluation with practical student risk stratification offers both methodological and applied contributions for academic monitoring and early intervention strategies in higher education.
Performance Analysis of VNC-Based Remote Monitoring for Flight Information Display Systems Using ANOVA and Regression Models
Purpose: This study evaluates the performance of a Virtual Network Computing (VNC)-based remote monitoring system within a Flight Information Display System (FIDS) environment. The research aims to identify infrastructure factors affecting monitoring performance and to develop a data-driven framework for evaluating monitoring reliability in distributed airport systems. Methods: Correlation analysis, Analysis of Variance (ANOVA), and multiple linear regression were applied to analyze the relationship between system resource utilization, network characteristics, and monitoring performance. The dataset consisted of 1,000 observations collected under various simulated monitoring conditions representing variations in latency, throughput, CPU utilization, and memory usage. Residual analysis and model evaluation were also performed to validate the statistical model. Result: The results showed that most infrastructure variables had very weak correlations (−0.02 to 0.05), indicating minimal multicollinearity. ANOVA testing revealed no statistically significant latency differences across low, medium, and high CPU load categories (F = 0.1625, p = 0.8500), with average latency remaining stable at approximately 52.82 ms. Regression evaluation demonstrated stable residual distribution and acceptable model consistency. The findings indicate that monitoring performance is influenced more by network conditions, particularly latency and throughput variability, than by computational load. Novelty: This study proposes an integrated analytical framework combining correlation analysis, ANOVA, and regression modeling to evaluate VNC-based monitoring performance in distributed systems. The framework provides a practical and reproducible approach for monitoring performance evaluation and infrastructure optimization in airport monitoring environments.
An Evaluation of AES-256 Based PDF Document Security Using Cryptographic Metrics and Blockchain Based Hash Storage
Purpose: This research aims to design and implement a digital document security by integrating the Advanced Encryption Standard (AES) 256-bit algorithm with blockchain technology to protect documents from manipulation, forgery, and information leakage. Methods: The study involved implementation using Python. AES-256 was applied to encrypt digital documents, while the hash value of the ciphertext was stored on the blockchain to ensure integrity and authenticity. System performance was evaluated through avalanche effect testing, entropy analysis, and process time measurement. Result: The average efficiency is 49.989% for small documents, 49.988% for documents 50-200 KB and 49.998% for documents larger than 200 KB, using good bits. An average entropy value of 7.99 indicates a high performance. The average sending time is 1.62 ms, and the average blockchain sending time is 1061.862 ms making it possible to send in sending digital documents. Novelty: This study integrates AES-256 encryption with blockchain-based hash storage to simultaneously ensure confidentiality, integrity, and authenticity of digital documents with high efficiency.
Region-Aware Double-Tail GAN with AdaIN–LADE Adaptive Style Transfer for Cinematic Anime Background Stylization in Makoto Shinkai Style
Purpose: Background stylization in a specific anime art direction remains challenging because global style transfer often yields inconsistent stylization across semantic regions. Our prior Double-Tail GAN (DTGAN) with Adaptive Instance Normalization (AdaIN) and Linearly Adaptive Denormalization (LADE) can produce Shinkai-like backgrounds, but it still exhibits region-specific failures such as unstable sky gradients, over-stylized vegetation textures, and reduced edge clarity in buildings. Methods: We propose a region-aware extension of DTGAN by conditioning the generator on semantic masks (sky, vegetation, and buildings) and optimizing with coverage-aware, region-weighted objectives. Semantic masks are generated automatically using a lightweight transformer-based semantic segmentation model and refined via simple morphological filtering to stabilize mask boundaries during training. Result: Experiments on real photographs and Makoto Shinkai-style background frames show that region-aware conditioning improves both global and region-level quality compared with DTGAN without masks. The proposed method reduces global FID from 74.5 to 65.8 and LPIPS from 0.505 to 0.448, while improving sky-gradient similarity and overall palette consistency. Novelty: This work contributes (i) a practical mask-conditioned DTGAN formulation for local controllability in cinematic anime background stylization, and (ii) a coverage-aware region-weighting strategy that mitigates over-stylization and style leakage when semantic regions occupy imbalanced areas, without requiring manual mask annotation.
Double-tail Generative Adversarial Network
Interpretable XGBoost-Based Explainable AI Model for Concrete Compressive Strength Prediction
Purpose: The objective of this research is to provide more accurate predictions and transparency in the analysis process, making it easier for engineering professionals to comprehensively interpret, validate, and evaluate the model results. Methods: The algorithm used is XGBoost with the XAI-SHAP approach, which serves to interpret the contribution of each input feature, like the water-cement ratio, cement content, and concrete age, to the predicted output. This approach ensures that the model is not only accurate but also open and accountable to engineering practitioners. The research methods are data collection, model training, SHAP visualization, and performance evaluation using RMSE, MAE, and R² metrics. The dataset used is the Concrete Compressive Strength dataset from the UCI Repository, which consists of 1,030 records. Result: Subsequent to model training using the XGBoost with optimal hyperparameters identified via Random Search, model performance was evaluated across three scenarios: training data, testing data, and 5-fold cross-validation. The results indicate that the proposed XGBoost exhibits show high performance with an RMSE 4.59 ± 0.63 and R² of 93% as well as stability, which is evidenced by consistent performance metrics, without notable signs of overfitting. Novelty: The novelty of this study lies in the combination of XGBoost with the XAI-SHAP approach for predicting concrete compressive strength, providing both accuracy and interpretability that can work with nonlinear data, as well as its ability to produce results that are easily understood by construction professionals, making it a favorable choice for predicting concrete compressive strength.
A Cross-Platform Assessment of Personal Data Protection Compliance among Electronic System Providers Under Indonesia’s PDP Law
Purpose: This study evaluates the level of Personal Data Protection (PDP) compliance among Electronic System Providers (ESPs) in Indonesia based on the Personal Data Protection Law (PDP Law) and the Government Regulation concerning the Implementation of Electronic Systems and Transactions. Methods: A descriptive-evaluative approach was conducted through observations of website and mobile application interfaces from 20 ESPs registered with the Ministry of Communication and Digital Affairs. Compliance was assessed using a binary scoring system based on six PDP indicators: consent mechanisms, privacy notices, TLS/SSL implementation, data disclosure, malicious libraries, and device data access. Descriptive statistical analysis was used to evaluate compliance levels. Instrument validity was established through content validity and expert judgment. Result: Most ESPs were classified within the moderate compliance category, covering 90% of websites and 80% of mobile applications. Governance-related indicators showed the lowest compliance levels, particularly website consent mechanisms (15%) and website privacy notices (40%) and mobile consent and privacy notice compliance (20%). In contrast, all ESPs complied with technical indicators, including TLS/SSL, malicious library, and device data access requirements. Novelty: Unlike previous studies that focused on single sectors or platforms, this study provides a cross-platform PDP compliance assessment integrating both technical and governance indicators within a single framework. The findings indicate that governance practices remain the primary challenge in PDP implementation, providing practical recommendations for regulators and ESPs in strengthening personal data protection implementation in Indonesia.
Boundary-Aware Learning for Glioma Detection in MRI Using YOLOv8 Segmentation Supervision
Purpose: Since glioma is the most aggressive and infiltrative type of brain tumor, its detection in magnetic resonance imaging (MRI) is especially difficult. Despite the excellent overall accuracy for brain tumor detection with YOLOv8-based object detection, the glioma-specific performance is limited owing to ambiguity of tumor boundaries. This work seeks to elucidate if boundary-aware learning can enhance glioma detection beyond typical bounding box–based approaches. Methods: This study focuses exclusively on glioma detection using the Cheng brain tumor MRI dataset. YOLOv8 is used as the baseline detector, and boundary-aware learning is implemented through segmentation supervision using YOLOv8-Seg by leveraging pixel-level tumor masks. All the experiments are done in a standardized training environment to allow fair and unbiased comparison. Result: Experimental evaluation shows that YOLOv8-Seg achieved a detection precision of 0.899, recall of 0.905, and mAP@50 of 0.940, while segmentation results achieved a mask precision of 0.900, recall of 0.904, and mAP@50 of 0.943. For glioma-specific analysis, the model achieved a box mAP@50 of 0.875 and a mask mAP@50 of 0.877. These results indicate that segmentation supervision improves spatial boundary representation even though improvements in conventional detection metrics remain marginal. Novelty: Unlike the other works that are based on augmentation of the data and performance of better detection, this work has devised a glioma-centric design, and shows bounding box-based detection is insufficient. This work highlights the need for considering boundary aware learning applying the supervision of segmentation in the automated glioma detection system, which can improve the reliability and interpretability of the system.
Good Governance Practices of COBIT 2019 and ITIL v4 for Sustainable Service Management in Museum Electronic-Based Government System
Purpose: Good governance practices in transparency, accountability, responsibility, fairness, and stakeholder participation are core principles that ensure governance mechanisms are not merely procedural, but also creates a sustainable service management environment that aligns governance conditions with public interests. Methods/Study design/approach: Through the integration of COBIT 2019 and ITIL v4 frameworks, along with structured staff interviews, service performance analysis, and direct operational observations, several important domains emerged, indicating the need for management development. Result/Findings: The results show that several COBIT 2019 domains, APO07 (Managed Human Resources), DSS01 (Managed Operations), BAI03 (Managed Solutions Identification and Build), EDM02 (Ensured Benefits Delivery), and MEA01 (Managed Performance and Conformance Monitoring) and ITIL v4 domains, Information Security Management, Organizational Change Management & Workforce, Continual Improvement require immediate improvement with the Capability Index scored 46.875% classified as Partially Achieved (15–49%) and classified in Level 2 (Managed Process). COBIT 2019 Design Factors show urgent areas for improvement in DF-4 (IT Related Issues), DF-6 (Compliance Requirements), and DF-7 (Role of IT). Balanced Scorecard (BSC) was also evaluated governance performance from four perspectives, financial, customer, internal process, and learning & growth, scoring 69,4% and classified as moderate. Key Performance Indicator (KPI) recommendations were also proposed, e.g., staff training increasement, operational IT services availability, user-based IT solutions, IT process measurement, security accidents reporting, effective workforce, and user-oriented IT developments. Novelty/Originality/Value: This study proposes an integrated COBIT 2019 and ITIL v4 approach, embedding good governance principles for sustainable service management in Museum Electronic-Based Government System (SPBE) and addressing the gap between governance conditions and public interests.
Electronic-based government system (SPBE)
Performance Evaluation of Distributed Lag, Autoencoder, and LSTM Autoencoder Methods in Detecting Anomalies in Simulated Data Based on Air Quality Index in Jakarta
Purpose: This study evaluates the performance of distributed lag, autoencoder, and LSTM autoencoder methods in detecting point anomalies in simulated data generated from Jakarta's Air Quality Index (AQI). The evaluation is conducted across several simulation scenarios that represent factors that may influence anomaly detection performance. Methods: Simulation scenarios were constructed by varying two anomaly characteristics: anomaly percentage (0.3%, 0.5%, and 1.0%) and anomaly depth (4.4σ, 4.7σ, and 5.0σ), yielding 90 datasets generated via repeated experiments. Anomaly detection was performed using a forecasting-based approach with a 4σ threshold on prediction errors. Model performance was evaluated using mean absolute percentage error (MAPE) for forecasting accuracy and balanced accuracy for anomaly detection. Result: Increasing anomaly percentage significantly degrades both forecasting and anomaly detection performance across all methods. In contrast, anomaly depth has no significant effect on forecasting accuracy but strongly influences detection performance. Among the evaluated methods, the distributed lag model consistently shows the most robust anomaly detection performance across scenarios, outperforming the autoencoder and LSTM autoencoder, particularly at higher anomaly percentages and depths. Novelty: This study introduces a more rigorous and structured evaluation framework for anomaly detection by integrating three key contributions. First, it provides a unified comparison of distributed lag, autoencoder, and LSTM autoencoder methods within a single experimental setting, a limitation in prior studies. Second, it employs a controlled simulation design that systematically varies the anomaly percentage and depth while preserving key characteristics of empirical AQI data, enabling a more objective assessment of model performance across diverse anomaly conditions. Third, it uses ANOVA and interaction analysis to formally examine the effects of anomaly characteristics on both forecasting and detection performance, moving beyond purely descriptive comparisons commonly used in previous research.
Towards Achieving Smart Healthcare Through the Adoption of Information Technology
Purpose: The study focuses on the impact of Virtual Reality as a therapeutic technology in the context of Smart Healthcare on the rehabilitation of hand amputees and the ability of out-patients to gain computer access in a cost-effective manner. Methods: A qualitative, descriptive bibliometric survey of the literature was undertaken, utilizing peer-reviewed literature relevant to the integration of virtual medical care (V-Med) with the IoT, EHR, and CDSS. The literature examined was categorized into two analytical domains - physical and clinical opportunities and technical challenges. Results: The evidence suggests that cloud focused Virtual Reality (VR) helps people with Phantom limb pain and reduces the time required for amputees to achieve the adaptation to a prosthesis. The disadvantages of VR technology will only be realized when VR technology, EHR and the Internet of Everything (IoE) systems coalesce. This is where the central issue of technical feasibility lies. Novelty: The presentation here outlines a shift in attitude towards Virtual Reality. Rather than isolating it from a continuum encompassing all virtualization in the cloud with amputee rehabilitation modalities, we embrace it. We propose a framework that integrates cloud-based IoT wearables, EHRs, and CDSS into a Smart Healthcare ecosystem, thus outlining a feasible, affordable scalable virtual rehabilitation path.
Factors Influencing the Adoption of Digital Waste Banks: A Systematic Literature Review
Purpose: This study aims to identify and analyze the factors influencing the adoption of digital waste banks, considering the gap between their potential to improve waste management and their limited implementation in many regions. Methods/Study design/approach: This research use a Systematic Literature Review (SLR) approach by analyzing 22 selected studies published between 2020 and 2025 from reputable databases. The study follows structured stages, including planning, literature selection based on inclusion and exclusion criteria, quality assessment, data extraction, and data synthesis using the PICOC framework. Result/Findings: The results show that there are 26 factors influencing the adoption of digital waste banks, which are grouped into four main categories: technology, organization, environment, and people. Technological factors include ease of use, transparency, system quality, and security. Organizational factors involve infrastructure, innovation, and human resources. Environmental factors include regulations, social influence, environmental awareness, and economic incentives. Meanwhile, people-related factors include age, education, income, and training. These factors are interconnected and play an important role in determining adoption success. Novelty/Originality/Value: This study provides a comprehensive classification of factors influencing digital waste bank adoption based on recent literature. The findings offer practical insights for policymakers and practitioners in designing strategies to increase community participation and improve sustainable waste management through digital innovation.
Hybrid Genetic Algorithm and Adaptive Momentum Backpropagation Model with Support Vector Regression Kernel Function for Short-Term Electricity Load Forecasting
Purpose: Short-term electricity load forecasting (STLF) plays an important role in power system management because electricity demand is dynamic and influenced by factors such as community activities, weather conditions, and seasonal patterns. However, conventional forecasting methods often have limitations in modeling nonlinear and fluctuating electricity load data. Therefore, this study aims to develop a more accurate and stable forecasting model by integrating artificial intelligence methods within a Graphical User Interface (GUI)-based system. Methods: This research proposes a hybrid model combining Genetic Algorithm (GA), Adaptive Momentum Backpropagation (AMBP), and Support Vector Regression (SVR). GA is used to optimize SVR parameters, SVR performs nonlinear regression forecasting, and AMBP improves learning stability. The dataset consists of electricity load data from Gunung Sari District, Lombok, collected during 2015–2024 with 3,650 daily samples, divided into 80% training data and 20% testing data. Model performance was evaluated using MSE, RMSE, and MAPE. Result: The experimental results show that the proposed GA–SVR–AMBP hybrid model achieves better forecasting performance than single and partial hybrid models. In the testing phase, the model produced an MSE of 0.1556, RMSE of 0.3945, and MAPE of 1.13%, with an accuracy of 98.86%. Using the entire dataset, the model achieved an MSE of 0.4213, RMSE of 0.6491, and MAPE of 1.6953% with an accuracy of 98.30%, indicating good generalization capability and low prediction error. Novelty: The novelty of this study lies in the development of a hybrid GA–SVR–AMBP forecasting model integrated into a MATLAB-based GUI system that facilitates data analysis, model execution, and visualization of prediction results for short-term electricity load forecasting and decision support in power system management.
Analysis of the Dominance of Natural Factors and Human Activities on Recurrent Floods on Sumatra Island Using SHAP-Based Random Forest
Purpose: The objectives of this work are to conduct a comprehensive analysis of the primary influences of both natural and anthropogenic causes on flooding in Kalimantan, utilising a transparent machine learning approach, and to provide policymakers with valuable insights for formulating strategies to mitigate flooding. Methods: The research employs the Random Forest model, integrating SHAP (Shapley Additive Explanations), to examine non-linear multivariate correlations and ascertain the significance of each variable. The data consists of both natural elements (precipitation, elevation, slope gradient, and proximity to the river) and anthropogenic activity (land fire hotspots, planting, mining, and building new infrastructure). The data also includes public sentiment data in text form. We got these data points per year from 2021 to 2025. We used R-squared and SHAP scores to figure out how accurate the model was. Result: The model has a high R² score of 0.81, which shows that it can make accurate predictions. Using SHAP (SHapley Additive exPlanations), we can see that natural factors, such as how far away the river is and how much it rains, are what make the area vulnerable in the first place. Human actions, on the other hand, are what cause the floods to happen again and again. The indicator for hotspots of land burning is the most important factor. Plantation and mining operations make up more than 90% of the overall contributions, followed by other predictors, in the case of flood-induced deforestation. Novelty: The current study proposes a framework for explicable AI that integrates the use of random forests and SHAP to assess the significance of various flood risk indicators via quantitative analysis of geographical and public opinion data.