Journal title Journal of Applied Data Sciences Initials JADS Abbreviation J. Appl. Data Sci. Online ISSN 2723-6471 Frequency 4 issues per year DOI doi.org/10.47738/jads Editor-in-chief Prof. Dr. Ibrahiem M. M. El Emary, (ID Scopus: 15135499400), King Abdulaziz University, Kingdom of Saudi Arabia Collaboration Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia. Publisher Bright Publisher Citation Analysis Scopus | Web of Science | Google Scholar Established in 2020, the Journal of Applied Data Science (JADS) aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data. The journal publishes research works on the full spectrum of the data science including statistics, computer science, and domain applications. The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data. The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science. Finally, accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases: Scopus Dimensions Google Scholar Index Copernicus GARUDA: Digital Reference Garba by Ristekdikti - Indonesia Indonesia OneSearch Crossref Search ROAD: the Directory of Open Access scholarly Resources OAI Address Journal of Applied Data Sciences has OAI address: https://bright-journal.org/Journal/index.php/JADS/oai Before submission, You have to make sure that your paper is prepared using the JADS paper Template, has been carefully proofread and polished, and conformed to the author guidelines. Online Submissions Already have a Username/Password for JADS? GO TO LOGIN Need a Username/Password? GO TO REGISTRATION Registration and login are required to submit items online and to check the status of current submissions. 7 days First Time Decision 93 days Review Time 100 days Submission to Accepted 32 days Acceptance to Publication
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The Effect of Globalization on Income Inequality in Developing Countries: A Bayesian Approach
The rapid advancement of economic globalization over the last several decades has sparked fierce disagreements about its impact on income inequality on a global and domestic scale. Whether globalization improves (neoclassical theory) or worsens (dependence theory) income inequality is a matter of debate at the theoretical level. The results of empirical studies have been contradictory as well. This study examines the effects of three lenses of globalization (financial openness, trade openness, and social globalization) on income inequality in developing countries. Using Bayesian estimation with Markov Chain Monte Carlo, we analyze a balanced panel of 36 developing countries from 2010 to 2022. The Bayesian method is particularly well-suited for social science research because of its capacity to effectively manage complex relationships and integrate prior information, resulting in more contextually relevant and robust results. The findings reveal significant nonlinear relationships between different dimensions of globalization and income inequality. Specifically, the impact of trade openness on income inequality is U-shaped, with a threshold of 83.35% of GDP, whereas the impact of direct foreign investment and migration is in an inverted-U shape, with respective thresholds of 13.4% of GDP and 1.276% of the total population. Importantly, all sampled countries remain below the identified thresholds for direct foreign investment and migration, indicating that these channels currently exacerbate inequality. Consequently, policy measures designed for “post-threshold” conditions should be viewed as forward-looking. This study contributes by clarifying how globalization can alternately worsen or reduce inequality depending on a country’s stage of integration. From a policy perspective, developing countries should strengthen absorptive capacity and institutional readiness so that higher direct foreign investment inflows and migration eventually yield more equitable outcomes once thresholds are surpassed. Meanwhile, countries already beyond the trade openness threshold should proceed cautiously, prioritizing export diversification, vocational training, and inclusive trade policies to mitigate inequality risks.
Assessing Large Language Models for Zero-Shot Dynamic Question Generation and Automated Leadership Competency Assessment
Automated interview systems powered by artificial intelligence often rely on fine-tuned models and annotated datasets, limiting their adaptability to new leadership competency frameworks. Large language models have shown potential for generating questions and assessing answers, yet their zero-shot performance, operating without task-specific retraining remains underexplored in leadership assessment. This study examines the zero-shot capability of two models, Qwen 32B and GPT-4o-mini, within a multi-turn self-interview framework. Both models dynamically generated questions, interpreted responses, and assigned scores across ten leadership competencies. Professionals representing the role of Digital Marketing and Account Manager participated, each completing two AI-led interview sessions. Model outputs were evaluated by certified experts using a structured rubric across three dimensions: quality of behavioral insights, relevance of follow-up questions, and fit of assigned scores. Results indicate that Qwen 32B generated richer insights than GPT-4o-mini (mean = 2.86 vs. 2.62; p less than 0.01) and provided more differentiated assessments across competencies. GPT-4o-mini produced more consistent follow-up questions but lacked depth in interpretation, often yielding generic outputs. Both models struggled with accurate scoring of candidate responses, reflected in low answer score ratings (Qwen mean = 2.35; GPT mean = 2.21). These findings suggest a trade-off between insight richness and scoring stability, with both models demonstrating limited ability to fully capture nuanced leadership behaviors. This study offers one of the first empirical benchmarks of zero-shot model performance in leadership interviews. It underscores both the promise and current limitations of deploying such systems for scalable assessment. Future research should explore competency-specific prompt strategies, fairness evaluation across demographic groups, and domain-adapted fine-tuning to improve accuracy, reliability, and ethical alignment in high-stakes recruitment contexts.
Hybrid Deep Learning for Image Authenticity: Distinguishing Between Real and AI-Generated Images
The increasing use of artificially generated images raises significant concerns about the authenticity of digital content. This study introduces a hybrid deep learning model for binary classification of real and generated images by combining spatial and relational features. The central idea is to integrate a convolutional backbone adapted from ResNet18 for visual feature extraction with a graph representation based on nearest-neighbor relations to capture inter-image similarities. The objective is to evaluate whether this dual-feature approach improves classification performance compared to single-feature baselines. Using a balanced dataset of 1,256 images (744 real and 512 generated), the model was trained on 70% of the data and tested on the remaining 30%. Experimental findings demonstrate that the model achieved an overall accuracy of 88%, with precision of 0.91 and recall of 0.89 for real images, and precision of 0.85 and recall of 0.87 for generated images. The corresponding F1 scores were 0.90 and 0.86, yielding a macro average F1 of 0.88. Confusion matrix analysis shows balanced misclassification across both classes, while stable performance across epochs indicates reliable learning behavior. Results confirm that the hybrid model achieves stronger classification effectiveness than convolution-only or graph-only baselines. The novelty of this work lies in demonstrating that the integration of spatial and relational learning provides a more robust framework for detecting synthetic images than single-modality approaches. The contribution of this research is both methodological, in proposing a hybrid architecture that unifies convolutional and graph-based learning, and practical, in providing empirical evidence that such integration enhances the reliability of image authenticity verification. While the absence of a validation set limited hyperparameter optimization and early stopping, the findings indicate that this hybrid design offers a promising direction for improving the robustness and generalizability of synthetic image detection.
Digital Financial Literacy, Entrepreneurial Competencies and Local Government Initiatives: Keys to SMEs’ Sustainability
This study examines the sustainability of Small and Medium-Sized Enterprises (SMEs’) in the Special Region of Yogyakarta, Indonesia, by assessing the combined effects of local government initiatives, digital financial literacy, and entrepreneurial competencies. The objective is to determine how these factors enhance long-term business viability in an increasingly digital economy. A mixed-method design was employed. In the qualitative phase, observations and in-depth interviews with officials from the Department of Industry and Trade explored the policy environment and SMEs’’ challenges, and themes from this stage were used to refine the survey instrument. In the quantitative phase, a structured survey of 338 SME owners measured digital financial literacy, entrepreneurial competencies, and sustainability practices. Data were analyzed using partial least squares structural equation modeling, including reliability and validity assessment, model fit evaluation, and bootstrapped hypothesis testing. Results show that both digital financial literacy (β = 0.546) and entrepreneurial competencies (β = 0.367) significantly influence SME sustainability, jointly explaining over 70% of the variance. The region’s policy environment—through initiatives such as incubation programs, Jogja Mark, the Sibakul Jogja platform, QR code payment adoption, and training—plays an enabling role in this relationship. The findings emphasize that digital financial literacy has the stronger effect, highlighting the importance of secure payment use, budgeting, and record-keeping for resilience and growth. The study contributes by integrating local policy, digital literacy, and entrepreneurial competency into a single empirical model, offering novel evidence on how these dimensions interact to support SME sustainability. The results provide actionable guidance for policymakers to strengthen digital financial capacity and tailor competency programs to sectoral needs.
SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering
Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
Assessing Consumer Perception in Muslim-made Cosmetics: A Relationship Quality Perspective
This study investigates the drivers of repurchase intention in the Muslim-made cosmetics market by examining the mediating roles of trust and satisfaction within a relationship quality framework. Grounded in Relationship Quality Theory and the Stereotype Content Model, the research integrates perceived product quality, brand image, and perceived AI warmth as key antecedents influencing consumer loyalty. A comparative analysis of two structural models (Model 1 and Model 2) is conducted to explore the directionality of the mutually reinforcing relationship between trust and satisfaction. Data were collected via structured online surveys from 439 Muslim consumers in Jakarta, Indonesia, and analyzed using PLS-SEM. Findings reveal that perceived AI warmth is the strongest predictor of both trust (t=7.587, p<0.001) and satisfaction (t=8.874, p<0.001). In Model 1, trust significantly precedes satisfaction (t=4.869, p<0.001); conversely, in Model 2, satisfaction reciprocally enhances trust (t=5.280, p<0.001), supporting a dynamic, co-evolutionary process. Perceived product quality significantly impacts trust (t=3.780, p<0.001) but only affects satisfaction when satisfaction is modeled as an antecedent to trust (Model 2: t=1.984, p=0.048). Brand image exerts a strong effect on satisfaction (Model 1: t=4.235, p<0.001; Model 2: t=4.855, p<0.001) but loses its direct path to trust in Model 2. Both trust and satisfaction have significant direct effects on repurchase intention (Model 1, Trust t=4.577, p<0.001, Satisfaction t=8.538, p<0.001; Model 2, Trust: t=4.630, p<0.001; Satisfaction t=8.130, p<0.001). The study validates the dual mediating roles of trust and satisfaction in translating perceptions of product quality, brand symbolism, and AI-induced warmth into behavioral loyalty. Theoretically, it advances a reciprocal, experience-driven model of relationship quality, extending prior discussions of reciprocity beyond traditional unidirectional frameworks. Practically, it offers actionable insights for marketers seeking to leverage AI personalization and identity-based branding to cultivate long-term loyalty in culturally sensitive, value-driven markets.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability
This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Time Series Forecasting of Environmental Dynamics in Urban Ecotourism Forest Using Deep Learning
Time Series Forecasting of Environmental Dynamics in urban forests is quite challenging, unless new approaches such as deep learning and remote sensing are employed. Deep learning-based time series algorithms offer robust scientific capabilities for forecasting and assessing sustainability trends using sequential data. Among these, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) have gained widespread adoption across various predictive modeling domains. In the present research, these algorithms are employed to analyze urban forest raster data derived from the Srengseng Ecotourism Forest, located in West Jakarta, Indonesia. The present study focuses on predicting the temporal patterns of key spatial indicators: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Forest Cover Density (FCD) in the Srengseng urban ecotourism forest area, spanning the years 2014 to 2024, through the application of LSTM, GRU, and BiLSTM deep learning architectures. The methodology used in this study is a combined approach involving remote sensing and deep learning. Spatial data were acquired through the delineation of a high-precision polygon of Srengseng Urban Forest using Google Earth Pro and Google Earth Engine (GEE). GeoTIFF datasets of NDVI, LST, and FCD for the years 2014–2024 were processed using Python-based modeling scripts. Model performance was evaluated through a comparative analysis of LSTM, GRU, and BiLSTM in predicting temporal trends in these ecological indicators. The results of this study show that the Bidirectional LSTM (BiLSTM) consistently demonstrated superior performance to predict all the data spatially, with scores of 0.94 for NDVI, 0.90 for FCD, and 0.85 for LST. Followed by LSTM that predicts NDVI (0.87), FCD (0.89), LST (0.83), as well as GRU, which can estimate spatial data NDVI (0.86), FCD (0.89), and LST (0.85). These results outperformed the predictive accuracy of both the standard LSTM and GRU models.
Unveiling the Pathways from Parenting to Entrepreneurship: A Structural Equation Modeling Approach
This study investigates how parenting styles influence vocational students’ entrepreneurial intentions and career choices, considering self-efficacy and entrepreneurial attitudes as mediating variables. Using a quantitative approach, data were collected from 381 vocational school students and analyzed with Structural Equation Modeling (SEM) using AMOS 24. The participants consisted of 55.7% females and 44.3% males, representing families from low-, middle-, and high-income groups based on the 2024 Jakarta provincial minimum wage, with parents working as civil servants, private-sector employees, entrepreneurs, and others. The results indicate that authoritative parenting positively fosters entrepreneurial intentions and encourages students to pursue entrepreneurship as a career path. Furthermore, the mediating roles of self-efficacy and entrepreneurial attitudes are confirmed, providing a clearer explanation of how parenting influences entrepreneurial career decisions. The study contributes theoretically by extending models of entrepreneurial intention with family socialization factors, and practically by offering a tested framework to guide efforts in promoting entrepreneurship among vocational students.
Applied Data Science for Sustainability Marketing: Evidence from Structural Equation Modeling of Organic Product Consumers
The global demand for organic products reflects increasing awareness of sustainability in consumer behavior, especially in emerging markets such as Indonesia. Despite this growing trend, limited studies have applied data science approaches to model behavioral relationships within sustainability marketing. This study aims to examine how Sustainable Marketing (SM) influences Prosumption Motivation (PM) and Consciousness for Sustainable Consumption (CSC) among Indonesian organic product consumers. Using a quantitative design, data were collected through purposive sampling, yielding 400 valid responses from participants across Java, Sumatra, Kalimantan, Sulawesi, and Bali. Structural Equation Modeling (SEM) with AMOS was employed as a data science tool to estimate latent constructs and test predictive relationships. The results show that SM has a significant positive effect on PM (β = 0.923, CR = 19.347, p < 0.001) and CSC (β = 0.991, CR = 21.764, p < 0.001), while PM also significantly influences CSC (β = 0.742, CR = 19.306, p < 0.001) and SM indirectly enhances CSC through PM (β = 0.652, CR = 19.306, p < 0.001). These findings confirm all hypotheses and reveal a reciprocal relationship between motivation and consciousness, emphasizing a behavioral feedback loop that strengthens sustainable consumption. The study contributes to sustainability marketing by integrating SM, PM, and CSC into a unified predictive framework. Methodologically, it demonstrates how SEM serves as an applied data science technique capable of transforming behavioral data into actionable insights. The novelty lies in bridging behavioral science and data science to provide decision-support evidence for marketers and policymakers promoting prosumption and responsible consumption in emerging economies.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method
Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
User Interface Design for Gamification of Ethnomathematics Data/Content Based on Challenges of Local Wisdom
Not all students possess the capability to effectively solve problems in accordance with the materials they have studied, both theoretically and practically, in reference to the realities of everyday life. This phenomenon is particularly evident in many junior high schools in Bali, especially within the context of mathematics education. Consequently, there is a pressing need for innovative breakthroughs to address these challenges. One such approach involves the development of gamified mathematics learning based on local Balinese wisdom. The primary objective of this research is to demonstrate the design of a user interface for gamified ethnomathematics data/content grounded in the challenges of local Balinese wisdom. This research adopts a development approach, employing the Borg and Gall model, which focuses solely on the stages of design development, initial testing, and revisions based on the outcomes of the initial trials. The subjects involved in the initial testing of the user interface design include two informatics experts, two education specialists, 40 educational technology evaluators, and 20 public junior high school teachers in Bali, particularly from the southern region. Data collection tools utilized an instrument in the form of a questionnaire consisting of 15 items related to the design of the user interface for gamified ethnomathematics data/content based on the challenges of local Balinese wisdom. The analysis of the collected data employed quantitative descriptive techniques.The research findings indicate that the quality of the user interface design is within the 'good' category, with an average percentage of 87.19%. The contribution and the most obvious implications of this research is that it can clearly demonstrate the form of gamification user interface design. Design that contains aspects of game data/content that internalize the challenges of the reality of Balinese local wisdom so that it can improve students' critical thinking skills in solving complex problems of everyday life.
Process Design of Software Library Development for Deep Learning Module in Java Programming with Four-Phase Methodology: Preparation, Identification, Design, and Development
Recent advances in deep learning have driven remarkable achievements across various domains, including computer vision, natural language processing, and medical diagnostics. However, prevailing DL libraries often expose monolithic and tightly coupled codebases, making it difficult for researchers to inject custom mathematical formulations into core training routines. To address this limitation, we introduce a modular software library that empowers users in both academia and industry to extend and modify training functions with minimal friction. This paper focuses on the preparatory stages of library development in Java Programming, presenting a four-phase methodology comprising Preparation (ideation, research questions, literature review), Identification (term extraction, goal definition, environment setup), Design (architecture modeling, class and attribute specification, task scheduling), and Development (component exploration, functionality construction). Through these sequential activities, we have produced eleven detailed design documents, including vision statements, quality-attribute scenarios, architectural decision records, and API specifications, that collectively capture the rationale and technical blueprint of our library. By sharing our step-by-step process, we aim to provide a replicable framework for future researchers undertaking the architectural design of specialized Deep Learning libraries.
Multiclass Skin Lesion Classification Algorithm using Attention-Based Vision Transformer with Metadata Fusion
Early and accurate classification of skin lesions is essential for timely diagnosis and treatment of skin cancer. This study presents a novel multiclass classification framework that integrates dermoscopic images with clinical metadata using an attention-based Vision Transformer (ViT) architecture. The proposed model incorporates a mutual-attention fusion mechanism to jointly learn from visual and tabular inputs, augmented by a class-aware metadata encoder and imbalance-sensitive loss function. Training was conducted using the HAM10000 dataset over 30 epochs with a batch size of 32, utilizing the Adam optimizer and a learning rate of 0.0001. The model demonstrated superior performance compared to a ViT Baseline, achieving 93.4% accuracy, 92.2% F1-score, 0.95 AUC, and significant reductions in MAE and RMSE. Additionally, Grad-CAM visualizations confirmed the model’s ability to focus on diagnostically relevant regions, enhancing interpretability. These findings suggest that the integration of structured clinical information with transformer-based visual analysis can significantly improve classification robustness, particularly in underrepresented lesion types. However, the model’s current performance is evaluated only on the HAM10000 dataset, and its generalizability to other clinical or non-dermoscopic image sources remains to be validated. Future studies should therefore explore multi-institutional datasets and real-world deployment scenarios to assess robustness and scalability. The proposed framework offers a practical, interpretable solution for AI-assisted skin lesion diagnosis and demonstrates strong potential for clinical deployment.
IndoBERT-SupCon: A Supervised Contrastive Learning Model for Analyzing Public Perception on Halal Tourism
The primary objective of this research is to develop and evaluate a robust deep learning model for accurately analyzing stakeholder perceptions of halal tourism development in Pariaman, West Sumatra, based on qualitative textual data. The main contribution is the introduction of IndoBERT-SupCon, a novel architecture that enhances the Indonesian BERT model with a Supervised Contrastive Learning (SupCon) mechanism. A novel method for producing more discriminative feature representations for complex viewpoints is presented in this paper, which is one of the first to use this sophisticated fine-tuning technique to Indonesian socio-political sentiment analysis. Conceptually, the model is trained to simultaneously minimize classification error while optimizing the feature space, pulling representations of similar sentiments closer together and pushing dissimilar ones further apart. To achieve this, we collected 1,022 primary textual responses through online surveys with tourists and in-depth interviews with key stakeholders, including SME owners and government officials. The SMOTE oversampling technique was employed on the training data to mitigate class imbalance. Experimental results on the test data demonstrate that the IndoBERT-SupCon model achieved outstanding performance, with a final accuracy of 96.59% and a macro F1-score of 0.97. These results significantly surpass the performance of a standard fine-tuned IndoBERT baseline, confirming the effectiveness of the SupCon approach. The findings provide the Pariaman local government with a highly valid, data-driven tool for more responsive and effective policy formulation. This research offers a robust framework that can be applied to other public policy domains, showcasing the value of advanced deep learning in transforming qualitative stakeholder feedback into actionable insights.
Data-Driven Forecasting of Special Education Enrollment: An Explainable Machine Learning Approach
The application of machine learning algorithms in the field of special education remains incipient despite advances achieved in other sectors. This field faces challenges related to inclusion, planning, and resource allocation, especially in contexts where administrative records are often underutilized for analytical purposes. This study proposes an explainable forecasting approach based on 23,905 historical data records to anticipate educational demand in the Special Basic Education (SBE) modality, aiming to develop and validate a Random Forest model applied to a multivariate database of official enrollment records from 2019 to 2024, projecting a slight global contraction from 28,000 to 26,800 enrollments by 2025. The findings reveal nonlinear growth patterns differentiated by region and educational level, mainly in Early SBE (ages 0 to 2), Preschool, and Primary, with a general trend of increasing demand in coastal and highland regions. The models achieved high levels of accuracy (R² > 0.97), with a Root Mean Squared Error (RMSE) below 190, a Mean Absolute Error (MAE) under 70, and a Mean Absolute Percentage Error (MAPE) below 10%. These results demonstrate the model’s utility as a strategic decision-support tool by optimizing resource planning in an education system characterized by territorial heterogeneity. The novelty of this study lies in integrating geospatial analysis and predictive algorithmic interpretability within an explainable artificial intelligence framework, fostering more equitable, transparent, and evidence-based educational planning.
A Stacking Ensemble Model for Predicting Student High School Graduation Outcomes
This study develops and evaluates machine learning models to predict high school graduation outcomes and identify at-risk students for early intervention. Using a quantitative approach, data from 1,017 students across three public high schools were analyzed, encompassing academic performance (average yearly scores), behavioral factors (attendance rates and extracurricular participation), and socio-economic background (proxied by parental occupation). A comparative modeling strategy was applied, beginning with a Decision Tree baseline and advancing to a Stacking Ensemble model that integrated three heterogeneous base learners—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree—combined through a Logistic Regression meta-model. Both models were optimized using GridSearchCV and adjusted for class imbalance between graduates (93.4%) and at-risk students (6.6%). The results showed that academic variables, particularly third-year average scores (mean = 82.6, SD = 6.4) and attendance rate (mean = 94.3%), were the strongest predictors of graduation, while socio-economic indicators had minimal impact. The Stacking Ensemble achieved a notable improvement over the Decision Tree, reaching an accuracy of 99.6%, precision of 0.909, recall of 1.000, F1-score of 0.952, and AUC of 1.000, compared to the baseline accuracy of 94.9% (F1-score = 0.519, AUC = 0.83). These findings indicate the superior predictive capability of the ensemble model in identifying students at risk of non-graduation. The study’s novelty lies in combining interpretable and high-performance models to construct a practical early-warning framework that can guide educators and policymakers in targeted academic interventions. However, the near-perfect metrics also suggest potential overfitting, emphasizing the need for validation using external datasets before broader application. Overall, this research contributes a robust, data-driven methodology for improving student retention through predictive analytics in educational settings.
Type Deep Learning Model for Multi-Label Waste Classification in Canal Environments: A Comparative Study with CNN Architectures
The escalating environmental degradation caused by waste underscores the necessity of developing intelligent and sustainable management systems. This study introduces a deep learning–based framework with proposed a modified ConvNeXt architecture enhanced by a two-layer non-linear MLP classification, specifically designed for multi-object waste classification in canal environments. Specifically, ConvNeXt-CNN is introduced as the primary backbone for extracting visual features from waste images. Then, a modified Multi-Layer Perceptron (MLP) is employed to transform these features into multi-label predictions. To optimize the model’s generalization capability in recognizing the complexity of waste images, a hybrid data augmentation technique combining SMOTE and MixUp was applied during training. The proposed approach was then compared with ten fine-tuned Convolutional Neural Network (CNN) architectures, ResNet18, ResNet50, VGG16, VGG19, DenseNet121, MobileNet_v2, and EfficientNet (B0, B1, B2, and B3), and evaluated using accuracy, precision, recall, and F1-score metrics. The experimental dataset comprises 855 waste images containing a total of 2,662 annotated objects across 18 categories, including Bamboo, Beverage Carton, Cardboard, Fabric, Glass Bottle, Inorganic Waste, Kite, Leaf, Metal, Organic Waste, Paper, Plastic, Plastic Bottle, Plastic Cup, Residual Waste, Rubber, Small E-waste, Styrofoam, and Wood. The results show that the fine-tuned ConvNeXt achieved the best performance with an F1-score of 0.99, surpassing DenseNet121 (0.95), ResNet18 (0.91), and VGG16 (0.94). The ConvNeXt model demonstrated its robust capability by achieving consistently high identification scores across majority 18 waste categories. When it came to training efficiency, the fine-tuned MobileNetV2 model proved to be the top performer, outclassing ten other pretrained models, with a training time of 13.35s per epoch. Results exhibit that finetuned ConvNext outperforms in terms of accuracy, recall, precision, and F1-score. In conclusion, Integrating ConvNeXt and MLP for multi-object waste classification effectively supports intelligent waste management, enabling practical real-world deployment in smart bins, Material Recovery Facilities, and IoT-integrated urban waste systems.
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning
Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
Data-Driven Evaluation of a Gamified Breath-Holding Training Application to Improve CT Scan Quality and Reduce Patient Anxiety
This study presents the development and evaluation of Breathe Well, an innovative three-tiered Graphical User Interface (GUI) application designed to address motion-induced step artifacts and patient anxiety during Computed Tomography (CT) scans. The core idea of the application is to combine relaxation techniques, guided breathing exercises, and gamified training modules within a single interactive platform that allows patients to practice breath-holding and anxiety control prior to scanning. The objective is to enhance patient cooperation, reduce involuntary movement, and improve overall image quality while minimizing the time healthcare staff spend on manual breath-hold instruction. The study involved a comparative analysis between a control group and an intervention group trained using the Breathe Well system. Quantitative results demonstrated a significant improvement in imaging outcomes, with the mean artifact score decreasing from 3.1 ± 0.8 in the control group to 2.1 ± 0.7 in the intervention group (p < 0.01). Psychological assessment using the State-Trait Anxiety Inventory (STAI) revealed a marked reduction in patient anxiety, with mean scores declining from 48.6 ± 6.4 before training to 38.2 ± 5.8 after using the application (p < 0.01). Qualitative feedback further confirmed increased patient confidence, comfort, and comprehension of CT procedures. The findings indicate that integrating gamified digital interventions into pre-scan preparation significantly improves both patient experience and diagnostic precision. The novelty of this research lies in the creation of a self-guided, multi-level digital platform that bridges behavioral training and imaging technology, offering a scalable, patient-centered solution for modern radiology workflows.
A Mixed-Methods Design for Analyzing Telemedicine Adoption: An Information Systems Approach Integrating TAM–ISS, Digital Literacy, and Usability
This research employs a mixed-methods design to analyze telemedicine adoption in rural Indonesia by integrating the Technology Acceptance Model (TAM) and the Information System Success Model (ISS), extended with digital literacy and usability. A quantitative survey of 314 respondents was complemented by in-depth interviews with 50 participants and demographic analysis using chi-square and logistic regression. The quantitative findings reveal that the primary adoption construct is Usability → Perceived Ease of Use (PEOU) → Perceived Usefulness (PU) → Intention to Use (ITU) → Net Benefit (NB). Perceived usefulness emerged as the strongest predictor of both satisfaction and intention. Information Quality significantly influenced satisfaction, whereas System Quality did not, indicating that clear medical information outweighs technical system performance in shaping satisfaction. Similarly, usability directly did not affect PU but indirectly through PEOU, and digital literacy influenced PU but not PEOU. Demographic analysis confirmed that occupation was significant—students and healthcare workers acted as early adopters—while age and prior training were not, suggesting that adoption transcends generational boundaries due to the widespread penetration of the JKN Mobile platform. Qualitative insights enriched these findings by highlighting key barriers and enablers such as inconsistent interfaces, infrastructure limitations, privacy concerns, community-based socialization, and expectations for adaptive features like AI diagnostics and pharmacy integration. Overall, the research confirms that telemedicine adoption in rural Indonesia is shaped by the synergy of usability, digital literacy, information quality, and social context rather than by training or demographic variables alone.
Applying Structural Equation Model to Explore the Impact of Performance Management on Business Performance at Small and Medium Enterprises
This study investigates how key organizational factors shape performance management and how performance management, in turn, enhances business performance among small and medium enterprises in Vietnam. The research aims to clarify the extent to which enterprise culture, internal communication, training and development, reward systems, and digital transformation influence managerial effectiveness and organizational outcomes. Building on strategic human resource management and organizational performance theories, the study develops and validates a structural model that integrates both human and technological dimensions of enterprise performance. A mixed-methods design was employed. The qualitative phase involved in-depth interviews with managers to refine the conceptual framework and contextualize measurement indicators. The quantitative phase surveyed 864 managers from small and medium enterprises across southern Vietnam to empirically test the model. Structural equation modeling was applied to examine the relationships among core organizational factors, performance management, and business performance, as well as the moderating effect of digital transformation. The findings show that training and development exert the most substantial positive influence on performance management, followed by reward systems, internal communication, and enterprise culture. Performance management significantly enhances business performance, confirming its role as a strategic mechanism that translates organizational capabilities into improved outcomes. Digital transformation directly improves business performance and enhances the impact of performance management by enabling real-time data use, transparency, and process efficiency. Overall, the study provides empirical evidence that strengthening human resource practices and integrating digital technologies are essential for improving performance management and fostering sustainable growth among Vietnamese small and medium enterprises. The results have important implications for managers and policymakers seeking to build performance-driven, digitally adaptive business environments in emerging economies.
The Application of Deep Learning in Qur’anic Tafsir Retrieval Using SBERT, FAISS and BERT-QA
Accurate understanding of the Qur’an requires access to reliable tafsir, yet many classical tafsir resources remain non-digital, making search and retrieval time-consuming. This study presents a semantic-based retrieval system for Tafsir Ibn Kathir, covering 114 entries and 6,236 Verses, using SBERT embeddings and FAISS indexing. The system enables users to perform semantic queries, retrieving relevant passages in response to their questions. Evaluation was conducted using 50 representative queries spanning diverse topics, including Fiqh, Aqidah, History, and Spirituality. Relevance judgments were independently provided by three Qur’anic studies experts and reconciled through discussion, with inter-annotator agreement indicating substantial consistency. Each query included 20 non-relevant passages as negative samples to increase evaluation difficulty. Two approaches were tested: retrieval-only and retrieval combined with a zero-shot QA module for span extraction. Retrieval-only achieved slightly higher top-1 accuracy (0.72), but retrieval + QA improved ranking-oriented metrics, including Accuracy@5 (0.88), Mean Reciprocal Rank (MRR = 0.76), and normalized Discounted Cumulative Gain at 5 (nDCG@5 = 0.82), with the increase in Accuracy@5 statistically significant (p = 0.01). The zero-shot QA module enabled the system to extract more precise and contextually relevant information, enhancing overall retrieval quality and robustness. These results indicate that the proposed system effectively retrieves relevant tafsir passages and provides accurate, context-specific answers. The study demonstrates the potential and limitations of zero-shot QA for domain-specific religious texts and supports the development of web-based applications or Islamic chatbots, facilitating easier access to shahih tafsir knowledge for scholars and the broader Muslim community.
Self-consistency and Graph-based Filtering to Enhance Synthetic Arabic SMS Generation for Smishing Detection
Smishing or SMS phishing is a growing cybersecurity threat in mobile security, with Arabic-speaking regions particularly vulnerable due to the absence of large, labeled datasets. The main objective of this study is to develop a scalable pipeline that can generate and classify Arabic SMS messages to overcome the lack of data and enhance detection performance. The contributions are threefold: (i) constructing a balanced dataset of 6,903 messages by combining 903 synthetic samples with 6,000 real Arabic SMS messages; (ii) introducing a hybrid generation framework that integrates a fine-tuned GPT-3.5-turbo language model with Conditional WGAN embeddings, refined using self-consistency sampling and graph-based redundancy filtering; and (iii) evaluating the dataset using multiple machine learning (Logistic Regression, Random Forest, SVM) and deep learning (CNN, BERT) models. The pipeline unifies adversarial embedding generation, large language model fine-tuning, and cosine similarity filtering. Experimental results show consistently strong performance: Logistic Regression and Random Forest both achieved accuracy of 0.9949 and F1-score of 0.9950, while SVM outperformed all with accuracy 0.9957 and F1-score 0.9957. Among deep learning models, CNN reached accuracy 0.9942 and F1-score 0.9942, and BERT achieved 0.9900 across all metrics. These findings confirm that while SVM is most effective for this dataset, CNN and BERT add robustness by capturing semantic subtleties. Visual analyses, including confusion matrices and t-SNE projections, validated the overlap between real and synthetic embeddings, while comparative tables positioned this study within the context of recent Arabic smishing research. The novelty of this work lies in combining self-consistency and graph-based filtering within a hybrid generation-classification pipeline tailored for Arabic SMS, providing a reproducible framework extendable to low-resource, multilingual, and cross-platform environments such as WhatsApp and Telegram.
MYCD: Integration of YOLO-CNN and DenseNet for Real-Time Road Damage Detection Based on Field Images
Road damage such as cracks, potholes, and uneven surfaces poses serious risks to transportation safety, logistics efficiency, and maintenance budgeting in Indonesia. Manual inspection is time consuming, labor intensive, and prone to error, motivating the use of reliable computer vision solutions. This study proposes MYCD, a hybrid and mobile ready architecture that combines the fast detection ability of YOLO with the dense feature reuse of DenseNet, enhanced by the Convolutional Block Attention Module (CBAM) for spatial and channel focus and Spatial Pyramid Pooling (SPP) for multi scale context understanding. The system detects and classifies the severity of road damage into minor, moderate, and severe categories using images captured by standard cameras. MYCD was trained and validated on 1,120 field images using an 80/20 split to simulate realistic deployment. Validation achieved 64 percent accuracy, with the highest per class precision of 0.72 for minor damage and mAP@0.5 = 0.677. The confusion matrix showed that most errors occurred in the moderate category because of visual similarity with minor and severe damage. Unlike earlier studies that extended YOLO with heavy backbones such as ResNet or EfficientNet, MYCD focuses on feature propagation (DenseNet), attention precision (CBAM), and multi scale fusion (SPP) optimized for real time operation on standard hardware. Efficiency profiling confirmed its deployability. After compression, the model size is 46.8 MB and it requires 3.7 GFLOPs per inference at 640×640 resolution. On a mid-range Android device (Snapdragon 778G, 8 GB RAM), MYCD runs at 19 frames per second with 1.2 GB peak memory. Compared with YOLOv8 WD (68 MB; 5.2 GFLOPs), MYCD reduces computation by 31 percent while maintaining similar accuracy. Overall, MYCD achieves a practical balance of speed, accuracy, and efficiency, providing a deployable and reproducible framework for real time road damage detection in resource limited settings.
Understanding User Needs for a Mobile Health Application: Insights into Fasting, Training, and Muscle Development
Mobile Health (mHealth) applications are increasingly used to support intermittent fasting, fitness training, and nutrition tracking. However, existing solutions remain fragmented, focusing on isolated domains without addressing users’ holistic health needs. This study aimed to explore user needs and preferences for an integrated mobile health application that combines fasting, training, and muscle development, emphasizing feature importance, usability expectations, and privacy concerns. A mixed-methods approach was used: a survey (n = 50) captured demographic profiles, feature prioritization, and usability expectations, while interviews (n = 10) explored user experiences and challenges. Quantitative data were analyzed using descriptive statistics, while qualitative interview responses were grouped into key themes through manual coding and interpretation. Results from both approaches were triangulated to strengthen the validity and reliability of findings. Users prioritized workout progression tracking (M = 4.94, SD = 0.18, 95% CI [4.89, 4.99]) and protein/macro monitoring (M = 4.20, SD = 0.42) over fasting timers (M = 2.92/5) or motivational features (M = 2.88). Usability expectations were high (Ease of Use = 6.06/7; System Capability Fit = 6.36/7), and privacy was a non-negotiable factor (M = 5.00/5). Themes revealed frustrations with incomplete exercise libraries, fragmented features, and lack of personalization. The study highlights the need for integrated, user-centered mHealth applications that unify fasting, training, and nutrition while embedding privacy-by-design principles. Future work will advance this study through prototype development and usability testing using SUS and UMUX-Lite metrics.
TF-EffBiGRU-AttNet: A Novel Deep Learning Framework for Spatio-Temporal Energy Demand Forecasting in Electric Vehicle Charging Networks
Electric Vehicle Charging Stations (EVCS) are key enablers of sustainable transportation, yet accurate forecasting of their energy demand remains challenging due to complex spatial-temporal variability. This study introduces a novel hybrid deep learning framework, Two-Fold EfficientNetV2 BiGRU with Attention (TF-EffBiGRU-AttNet), optimized using the Self-Adaptive Hippopotamus Optimization Algorithm (SA-HOA), to enhance prediction accuracy and computational efficiency in EVCS energy demand forecasting. The main objective is to integrate multi-scale spatial learning, bidirectional temporal modeling, and adaptive feature prioritization within a single architecture capable of robust and interpretable forecasting. The model’s novelty lies in its dual-fold spatial feature extraction using EfficientNetV2 and dynamic optimization through SA-HOA, which adaptively balances exploration and exploitation during training. Experimental validation on two real-world datasets from Palo Alto and Perth demonstrates that the proposed model consistently outperforms state-of-the-art baselines. For the 7-1 forecasting task, TF-EffBiGRU-AttNet achieved the lowest MAE of 0.012 and RMSE of 0.051 for Palo Alto, and MAE of 0.029 with RMSE of 0.12 for Perth. For the 30-7 task, it achieved MAE of 0.0332, RMSE of 0.1654, and MAPE of 0.20% on Palo Alto, and MAE of 0.0235, RMSE of 0.0824, and MAPE of 0.37% on Perth, outperforming Bi-LSTM and EfficientNet by over 60% in RMSE reduction. Moreover, SA-HOA improved optimization efficiency with a best fitness value of 0.0003 and reduced convergence time to 1.2 seconds, surpassing PSO, GWO, and HOA. These results highlight the framework’s ability to capture spatial-seasonal and nonlinear dependencies while maintaining low computational overhead. The findings confirm the model’s potential as a robust, adaptive, and scalable solution for intelligent EV energy demand forecasting, supporting smart grid planning and sustainable energy management.
Image-Based Detection of Reduced Security Features in Indonesian Banknotes Using U-Net Architecture
The circulation of fake currency banknotes in Indonesia continues to rise alongside rapid technological advancements, while conventional verification systems remain limited and often ineffective in detecting subtle authenticity cues. The main objective of this study is to develop an image-based fake currency detection system using the U-Net deep learning architecture and its modified version, T-Net, to enhance feature extraction and classification accuracy. The key contribution of this research lies in combining convolutional architectures with a practical, web-based interface that enables real-time image analysis, thus bridging the gap between model performance and user accessibility. A quantitative experimental method was employed, involving model development in Python using TensorFlow and Keras, and implementation of a Flask-based web application for real-time classification. The research utilized a dataset of 2,141 Indonesian rupiah banknote images, consisting of 1,015 genuine and 1,126 fake currency samples synthetically generated through digital modification of security features such as watermarks and color-shifting ink. Image preprocessing included resizing, normalization, and augmentation techniques such as random flipping and brightness adjustment to enhance data quality. Three convolutional architectures U-Net, ResNet-50, and the modified T-Net were trained and compared using identical hyperparameters. The T-Net model achieved the best performance, with 97.8% training accuracy, 82.6% validation accuracy, precision of 0.83, recall of 0.80, and an F1-score of 0.81. Despite the performance gap indicating overfitting, the model effectively distinguishes genuine from fake currency notes. The Flask-based interface allows users to upload images and receive classification results from all three models within 0.3–1.8 seconds per image. The findings demonstrate the feasibility and efficiency of U-Net based architectures for image-driven fake currency detection and provide a foundation for developing advanced, reliable, and real-time financial authentication systems that can strengthen digital security infrastructures in future applications.
Model Integration of Information Technology in Optimizing the Food Supply Chain of the Free Nutritious Meal (MBG) Program to Reduce Food Waste
The Free Nutritious Meal Program in Surabaya is a major government initiative designed to improve child nutrition and reduce hunger among schoolchildren from low-income families. Despite its importance, the program faces significant challenges of food loss and waste due to inefficiencies in transportation, storage, and demand matching. This study introduces a Smart MBG Cloud Platform and applies a linear programming model to optimize the program’s supply chain under two operational scenarios: a baseline system without Information Technology (IT) support and an IT-enhanced system integrating route optimization and digital inventory monitoring. Simulation results reveal substantial efficiency gains in the IT-integrated model. This study was conducted using a mixed-method approach involving samples from schools as beneficiaries and the Nutrition Fulfillment Service Unit as providers of free nutritious meals. Using simulation data from five kitchens and ten schools and conducting 50 stochastic replications, the IT-enhanced model achieved a 28% reduction in transportation cost and the total objective value declined by 22%, compared to without IT support scenario. These results demonstrate that incorporating digital route planning and inventory monitoring not only reduces operational expenses but also mitigates organic waste, ensuring fresher meal delivery and supporting sustainability targets. These improvements highlight the potential of digital tools to minimize inefficiencies, ensure fresher meal delivery, and strengthen the nutritional impact of the program. Beyond operational savings, the IT-based model contributes to reduced organic waste generation and aligns with broader sustainability goals. The findings provide empirical evidence that digital transformation can significantly enhance the performance of public food programs and offer practical insights for policymakers to replicate these strategies in similar urban initiatives.
Applied Data Science for Testing the Impact of Intangible Resources on Business Performance of SMEs
This study investigates how intangible resources influence business performance in small and medium-sized enterprises in Vietnam by applying structural equation modeling within an applied data science framework. The research aims to clarify the direct and indirect mechanisms through which key intangible components, such as human capital, structural capital, relationship capital, organizational culture, and brand image, shape enterprise outcomes. It also examines the mediating role of creative innovation and the moderating influence of operating time. The study employs a mixed-method design, beginning with qualitative interviews with enterprise managers to validate and refine measurement constructs, followed by a quantitative survey of managers in two major economic regions in Vietnam. Data were analyzed using an advanced structural modeling approach to assess reliability, validity, and the strength of causal relationships. The findings demonstrate that intangible resources act as a robust foundation for firm success, exerting strong positive effects on both creative innovation and overall business performance. All five resource dimensions significantly contribute to the higher-order construct, with relationship capital and organizational culture emerging as the most influential drivers. Creative innovation partially mediates the relationship between intangible resources and business performance, illustrating how firms convert knowledge-based assets into tangible outcomes through idea generation and implementation. Further, operating time strengthens this relationship, indicating that more established firms leverage their intangible foundations more effectively. The study contributes to ongoing discussions on resource-based competitiveness by extending theoretical perspectives to an emerging market context. It highlights the strategic importance of cultivating intangible resources to foster innovation capability and sustain long-term performance. The results offer practical implications for managers and policymakers seeking to develop knowledge-driven and innovation-oriented enterprises in dynamic economic environments.
Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection
The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
Predicting Gender from Online Dating Self-Introductions Using Machine Learning, Deep Learning, and DistilBERT
This study investigates a novel approach to automated gender classification in online dating profiles by comparing models that span traditional machine learning, deep learning, and transformer-based architectures. The dataset consists of self-introduction essays from publicly accessible repositories and enriched with psychological features (LIWC), lexical features (bag-of-words), and contextual representations (raw text). The primary objective is to evaluate predictive performance, robustness, and computational cost across these modeling strategies and to assess their trade-offs. A comprehensive preprocessing pipeline was implemented, including missing-value handling, text cleaning, LIWC feature extraction, Bag-of-Words vectorization, one-hot encoding of categorical variables, and class-imbalance mitigation through random oversampling. Text augmentation using synonym replacement was subsequently applied to increase data diversity while maintaining realistic linguistic patterns. Stratified five-fold cross-validation was used for traditional models and LIWC-only deep learning experiments, and StratifiedKFold (k = 5) was applied to LIWC + BoW configurations to ensure balanced splits. DistilBERT was fine-tuned on raw essay data using an 80/20 train–test split under GPU memory and batch-size constraints. Across three runs, DistilBERT achieved an average testing accuracy of 91% ± 1%, with precision, recall, F1-score, and ROC–AUC indicating balanced performance. A GRU trained on LIWC+BoW features reached 88.62% ± 0.53% accuracy, offering competitive results at substantially lower computational cost. An MLP trained solely on LIWC features provided a stable and interpretable baseline. Confusion matrices showed balanced predictions between male and female classes, highlighting the importance of feature representation and model selection. Overall, the findings demonstrate clear trade-offs between computational demand and semantic modeling capability. These results contribute to ongoing research on gender identification and guide future work on fairness, robustness, and explainability in AI-assisted user profiling. The study also underscores practical benefits for automated analysis of unstructured text in social and psychological applications, while recognizing ethical considerations related to non-binary and gender-fluid individuals.
Enhancing the Robustness of Adaptive Class Activation Mapping (AD-CAM) Against Noisy Facial Expression Data Using Preprocessing and Adaptive Normalization
In real-world computer vision applications, visual data is often corrupted by noise, reducing both the accuracy and interpretability of deep learning models. This study proposes an enhanced AD-CAM framework that integrates noise-aware preprocessing and adaptive normalization to improve robustness in both prediction and visual explanation. Experiments were conducted on the FER2013 facial expression dataset augmented with Gaussian, salt-and-pepper, and speckle noise. Using ResNet-50 as the backbone, the proposed method demonstrated significant gains across multiple evaluation metrics, including Robust Accuracy (RA), Drop Coherence (DC), Area Under Robustness Curve (AURC), and Signal-to-Noise Ratio (SNR). Compared to the baseline, the model achieved over 10% accuracy improvement and up to 0.16 DC reduction under noise. Qualitative visualizations showed that the improved model consistently highlighted semantically relevant facial regions, maintaining interpretability even under severe input degradation. These results support the adoption of noise-aware interpretability frameworks for more reliable and trustworthy deployment in real-world vision systems.
CNN-LSTM with Multi-Acoustic Features for Automatic Tajweed Mad Rule Classification
The rules of mad recitation in the Qur’an are a crucial aspect of tajwīd, governing the lengthening of vowel sounds that affect both meaning and recitational accuracy. Despite its importance, there is currently no reliable automatic system capable of classifying mad rules based on voice input. This study proposes a deep learning-based approach using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to automatically classify mad rules from Qur’anic recitations. The research follows the CRISP-DM methodology, covering data understanding, preparation, modeling, and evaluation stages. Acoustic features were extracted from 3,816 annotated audio segments of Surah Al-Fātiḥah, combining Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Root Mean Square (RMS) to represent phonetic and prosodic attributes. The CNN layers captured spatial characteristics of the spectrum, while LSTM layers modeled temporal dependencies of the audio. Experimental results show that the combination of all four features achieved an accuracy of 97.21%, precision of 95.28%, recall of 95.22%, and F1-score of 95.25%. These findings indicate that multi-feature integration enhances model robustness and interpretability. The proposed CNN-LSTM framework demonstrates potential for practical deployment in voice-based tajwīd learning tools and contributes to the broader field of Qur’anic speech recognition by offering a systematic, ethically grounded, and data-driven approach to mad classification.
Comparative Study of CNN-Based Architectures for Early Brain Tumor Diagnosis
This study presents a comprehensive comparative analysis of Convolutional Neural Network (CNN)-based deep learning architectures for early brain tumor detection and classification using multi-modal medical imaging. The primary objective is to evaluate and integrate advanced deep neural network models, including EfficientNet-B2, VGG16, U-Net, and a hybrid CNN-LSTM, to enhance diagnostic accuracy, precision, and robustness. The proposed framework involves five key stages: image acquisition from MRI, CT, PET, and ultrasound modalities; preprocessing through normalization, skull stripping, noise reduction, and registration; segmentation of tumor regions; feature extraction; and classification using optimized deep learning algorithms. Experimental evaluation demonstrates that the hybrid CNN-LSTM model achieved the highest overall performance, with an accuracy of 98.81%, precision of 98.90%, recall of 98.90%, and F1-score of 99%. The EfficientNet-B2 model followed closely with 98.73% accuracy, 98.73% precision, 99.13% recall, and 98.79% F1-score, confirming its strength in efficient feature utilization and computational scalability. In contrast, VGG16 and U-Net achieved accuracies of 93.27% and 88%, respectively, indicating limited adaptability to complex tumor morphologies. The findings reveal that CNN-based hybrid models outperform traditional architectures by effectively capturing both spatial and temporal dependencies in MRI data, leading to improved interpretability and clinical reliability. The novelty of this research lies in its methodological integration of convolutional and recurrent layers within a unified diagnostic framework, establishing a reproducible, high-performance model for early brain tumor detection. The study contributes to the advancement of intelligent medical imaging systems by demonstrating that hybrid deep learning architectures can significantly reduce diagnostic uncertainty and enable more precise, automated clinical decision support for early intervention.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques
This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
GAN-Enhanced Radial Basis Function Networks for Improved Landslide Susceptibility Mapping
Landslide susceptibility modeling is a critical task for disaster mitigation, yet it is frequently undermined by a severe class imbalance inherent in landslide datasets, where non-landslide instances vastly outnumber actual landslide events. This imbalance leads to biased machine learning models with poor predictive power for the minority (landslide) class, resulting in unreliable hazard maps. This study, focusing on the high-risk area of Malang Regency, Indonesia, addresses this challenge by proposing an innovative framework that integrates a Generative Adversarial Network (GAN) for synthetic data augmentation with a Radial Basis Function Network (RBFN) for classification. A highly imbalanced dataset with a 1:10 ratio of landslide to non-landslide points was constructed to establish a realistic baseline. On this data, the RBFN model, while theoretically powerful for capturing non-linear relationships, failed completely, achieving a Recall of 0.00 for the landslide class. The novelty of this research lies in the specific application of a GAN, trained for 15,000 epochs, to generate high-fidelity synthetic landslide data, thereby creating a perfectly balanced training set. After retraining on this augmented data and undergoing a systematic hyperparameter tuning process, the RBFN’s performance was dramatically transformed. The optimized model achieved an F1-Score of 0.9333 and a Recall of 0.8750, elevating its performance from total failure to a level competitive with the robust Random Forest benchmark. This work validates that the integrated GAN-RBFN approach is a highly effective methodology for overcoming the data imbalance problem in geospatial hazard modeling. By turning a previously unreliable classifier into a powerful predictive tool, this method has significant practical implications for developing more accurate landslide susceptibility maps, which are crucial for informed spatial planning and enhancing early warning systems.
Software to predict maternal and child health risks with machine learning
Objective: Maternal healthcare services are essential in public health, prioritizing the health and well-being of women throughout pregnancy, childbirth, as well as the postpartum period. The services include various efforts to safeguard the health of both the mother and the unborn child. During these stages, mothers face numerous risks and complications, making early risk detection critical for ensuring the safety of the pregnancy. Method: A novel method is needed that enables more accurate and affordable screening to improve early detection as well as increase maternal and child healthcare. Therefore, this study aimed to propose a solution including the development of software that uses the Naive Bayes algorithm to predict maternal and child health risks. The perceptions provided by the application served as an initial diagnostic reference for both expectant mothers and healthcare providers, offering a cost-effective as well as precise alternative. Result: During the analysis, the Naive Bayes algorithm was compared with Neural Network (NN) and Random Forest (RF) models to evaluate the prediction accuracy. Among the models used, NN produced the lowest accuracy at 48%. Conclusion: The estimated cost for developing this application was IDR 1,635,913.
Multimodal AI Framework for Sign Language Recognition and Medical Informatics in Hearing-Impaired Patients
This study assesses the feasibility of YOLO-based detectors for the recognition of Thai Sign Language (TSL) within clinical intake workflows. We benchmark YOLOv5 through YOLOv10 over 100 to 150 training epochs and evaluate metrics including Precision, Recall, mAP@50, mAP@50:95, alongside training and validation losses to gauge stability. The losses decrease steadily as detection metrics improve; YOLOv10 offers the optimal balance, with Precision at 0.953, Recall at 0.939, mAP@50 at 0.933, and mAP@50:95 at 0.492. The improvements observed at stricter IoU thresholds are modest, underscoring ongoing challenges in achieving accurate localization and generalization across varying lighting conditions, viewpoints, occlusions, and motion. YOLOv11 has been excluded from the primary results due to abnormal loss behavior. These findings endorse a multimodal pipeline that employs an image-based detector as the central perception component, supplemented with pose and key point cues, as well as OCR and NLP layers, to transform recognized signs into structured medical intents for triage and telemedicine applications. Future research will focus on expanding sequence-level evaluation, incorporating dialects and co-articulation in TSL, and developing compressed or distilled models to facilitate reliable on-device inference in resource-constrained environments.
Gamified Digital Intervention to Reduce Online Game Gambling Tendency among Youth: A TAM–SDT Evaluation
The rapid growth of online gaming has raised concerns about addictive behaviors among young people, particularly with the emergence of loot boxes that resemble gambling mechanisms. This study aims to examine the effectiveness of a gamification-based application as a preventive intervention for online gambling game addiction and to evaluate user acceptance through an extended Technology Acceptance Model (TAM). The research was conducted in two stages. In the pre-test phase, 588 respondents aged 15–25 completed a questionnaire measuring impulsivity, Internet Gaming Disorder (IGD), and loot box exposure. The results identified 169 individuals (28.7%) with addictive tendencies. In the intervention phase, 86 respondents from this group participated in a gamified stimulation program using a specially designed application. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model met reliability and validity requirements. Structural model analysis confirmed the classic TAM relationships: perceived ease of use significantly influenced perceived usefulness (β = .559, p < 0.001), perceived usefulness influenced attitude toward use (β = .385, P= 0.001), and attitude influenced behavioral intention (β = .461, p < 0.001). In addition, self-determination theory (SDT) significantly affected both attitude (β = .360, P= 0.003) and behavioral intention (β = .166, P= 0.038). However, affective visual design (AVD) was not significant, and behavioral intention did not reduce addictive behavior (β = -0.109, P= 0.386). The model demonstrated predictive relevance for TAM constructs (Q² > 0) but failed to predict addictive behavior (Q² = -0.003). This study contributes theoretically by extending TAM with SDT in the context of digital health interventions and practically by demonstrating the potential of gamification as a preventive tool. However, the short intervention period and limited sample size constrained its effectiveness in reducing addiction. Longer-term interventions and broader contextual factors are recommended for future research.
Lightweight Brain Tumor Classification with Histogram Oriented Gradients (HOG) Features and Class-Weighted Support Vector Machine (SVM)
Early detection of brain tumors via MRI is crucial for improving patient outcomes. This study investigates a lightweight machine learning approach for multiclass brain tumor classification (glioma, meningioma, pituitary tumor, or no tumor) using Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier. This study utilizes the public Brain Tumor Classification MRI Kaggle dataset, consisting of 2870 training and 394 testing MRI images across four classes. After converting the MRIs to grayscale and resizing them to 16×16 pixels, this study extracts HOG features and applies Principal Component Analysis (PCA) to retain 98% of the variance. An SVM is then trained with a GridSearchCV-optimized kernel and hyperparameters, and a custom class-weighted variant is compared. The best model, a polynomial-kernel SVM with custom class weights, achieved 91.8% test accuracy (95% CI (confidence interval): 90.9-92.7) with an F1-score of 0.919 ± 0.01, outperforming the best unweighted SVM (accuracy 86.0% ± 0.02, F1≈0.847). These results demonstrate that HOG+SVM, with proper weighting for class imbalance, can effectively classify brain tumors on small datasets at low computational cost. The novelty of this work lies in demonstrating that an optimized, class-weighted SVM leveraging compact HOG-PCA features can deliver over 91.8% accuracy with strong generalization on small-scale MRI data, providing a viable and interpretable alternative to complex Convolutional Neural Network (CNN) models. Future work can explore CNN and hybrid feature fusion to improve accuracy and generalization further.
Financial Inclusion as a Pathway to Sustainable Development: Regional Evidence from ASEAN and the Case of Vietnam
This study investigates the impact of Financial Inclusion (FI) on Sustainable Development (SD) in ASEAN countries over the period 2004–2022, aiming to provide robust regional and country-level empirical evidence using a Bayesian econometric framework. The core idea of the study is that inclusive financial systems serve as a critical transmission channel through which economic, social, and environmental dimensions of sustainable development can be jointly enhanced. The primary objective is to quantify the effect of FI on SD at the ASEAN level and to examine whether this relationship remains consistent when regional evidence is incorporated as prior information for country-level estimation. Using Bayesian regression, the results reveal a strong and positive effect of FI on SD, with an estimated posterior mean coefficient of 15.531 for ASEAN countries and 15.5448 for Vietnam. The posterior probability associated with these coefficients is approximately 1.000 (≈100%), indicating an extremely high level of statistical confidence in the positive FI–SD relationship. Markov Chain Monte Carlo (MCMC) diagnostics confirm convergence and parameter stability across iterations, supporting the reliability of the Bayesian estimates. Robustness checks using Pooled Ordinary Least Squares (POLS) and Feasible Generalized Least Squares (FGLS) yield consistent coefficient signs and magnitudes, further validating the main findings. The results demonstrate that economies with higher levels of financial inclusion tend to achieve significantly better sustainable development performance. The key contribution and novelty of this study lie in its Bayesian regional-to-country estimation strategy, which integrates ASEAN-level evidence as informative priors to strengthen country-specific inference, thereby offering new methodological and policy-relevant insights into the finance–sustainability nexus.
Navigating Institutional Pressures and Cognitive Aspects: Strategies for Sustainable Corporate Performance in Creative Industries
This study examines the relationship between institutional pressures and corporate performance through the lens of corporate cognitive aspects in the Indonesian creative industry sector. Data were collected from 531 business owners across fashion, food, and crafts sectors and analyzed using Structural Equation Modeling Partial Least Squares (SEM–PLS). The results show that institutional pressures significantly affect digital transformation (β = 0.656, p < 0.001), entrepreneurial orientation (β = 0.748, p < 0.001), dynamic capability (β = 0.660, p < 0.001), and competitive advantage (β = 0.759, p < 0.001). However, the direct effect of institutional pressures on corporate performance is not significant (β = 0.039, p = 0.231). Instead, digital transformation (β = 0.347, p < 0.001), entrepreneurial orientation (β = 0.146, p = 0.007), and dynamic capability (β = 0.446, p < 0.001) mediate the relationship, explaining 65% of the variance in corporate performance (R² = 0.65). This study contributes to the literature by highlighting the role of corporate cognitive aspects and emphasizing the integration of institutional theory into entrepreneurial practice. Moreover, it aligns with Sustainable Development Goals (SDGs) 9 and 12 by promoting responsible consumption and innovation in industry. One limitation of this study lies in its focus on Indonesia’s creative sector, suggesting that future research should explore broader contexts. The findings encourage businesses to adopt corporate cognitive aspects to achieve sustainable growth. Future studies could also investigate additional antecedents of corporate cognitive aspects and their influence on environmental performance.
A Dual Pathway of Governmental Support for SME Performance: Paradoxical Impact of Organizational Learning Capabilities
Purpose: The Global market is creating rising tensions and uncertainty for businesses, especially for Small and Medium Enterprises (SMEs). which must be addressed by the government. Despite the critical role of technological innovation, significant economic hurdles for SMEs remain. This research explores the mechanism by which government support contributes to SMEs' performance through technological innovation, and further examines the moderating role of Organizational Learning Capabilities (OLC). Design/methodology/approach: A quantitative method was adopted using SPSS and SmartPLS 4.0 software to evaluate the research model. Data was collected through a survey-based questionnaire completed by leaders and senior managers representing 450 Vietnamese SMEs. Findings: Analytical results reveal that government support exerts a significant direct influence on SME performance, while its effects are partially transmitted through technological innovation, indicating a dual pathway that enhances overall outcomes. Interestingly, the moderating role of organizational learning capability is confirmed. Specifically, it strengthens the translation of government support into technological innovation but simultaneously weakens the positive association between technological innovation and SME performance. Implications: SME managers in developing countries should prioritize innovation-focused as phase one, followed by market-focused in phase two to capture the support of governments. Furthermore, SMEs also prepare resource slack for past reflection and analyze gaps between innovation practices and financial returns to provide real-time modifications to reduce negative outcomes from OLC.
A Practical YOLO Approach to Classifying Thai Freshwater Snails of Economic Significance
Freshwater snails are a valuable economic resource in Thailand, but species identification remains challenging due to morphological similarities that impact pricing, traceability, and aquaculture management. This study assesses an automated freshwater snail classification system using three YOLO variants trained for 100 epochs on 4,610 annotated images of six economically important species. The models were evaluated using precision, recall, mAP50, mAP50–95, inference time, and model size, revealing clear performance trade-offs. YOLOv9-tiny achieved the highest detection accuracy with an mAP50–95 of 0.9738 but incurred the largest model size and slowest inference. In contrast, YOLOv11-nano delivered the fastest inference and smallest footprint, though with lower accuracy (mAP50–95 of 0.8849), making it suitable for resource-limited or edge deployments. YOLOv8 provided a balanced alternative, offering competitive accuracy (mAP50–95 of 0.9708) with moderate computational cost. Misclassification most occurred between Bellamya sp. and Bellamya reticulata, particularly for juvenile specimens, highlighting the difficulty of distinguishing morphologically similar species and the need for more diverse training data. Overall, the results demonstrate the effectiveness of YOLO-based models for automated snail species identification, with strong potential for applications in aquaculture management, market standardization, and supply chain traceability. Future work will focus on real-world deployment, expanding datasets across diverse environments, and integrating explainable AI to improve model transparency and user trust.
Quantum-Inspired Optimization for Traffic Congestion: A QUBO-Based Approach with Simulated Annealing
Urban traffic congestion remains a persistent challenge, especially when road segments exceed vehicle capacity, leading to increased travel times and road density. This study introduces a new QUBO framework designed to dynamically reduce congestion by optimizing vehicle routes while considering the capacity constraints of road segments. The proposed model establishes quadratic penalties for road segments that exceed the set capacity thresholds, providing incentives to redistribute vehicles to alternative routes while maintaining overall traffic flow efficiency. The QUBO formulation also incorporates road density as a factor to distribute vehicle routing more evenly. The challenge is to ensure that the chosen route does not create potential congestion on the next road segment. We conducted the simulation on a road network consisting of 15 segments (edges) to effectively manage up to 21 vehicles in dense traffic. This QUBO model was created using a quantum annealing approach, but its execution was carried out on an annealing simulation with the Fixstars Amplify and D-Wave Neal machines. The results indicate that the proposed QUBO congestion model can maintain road segment density between 60% and 80% across almost all segment routes. The QUBO congestion model is capable of distributing vehicles evenly, with a Gini coefficient reaching 0.0496 (in an experiment with 21 vehicles), which has the potential to reduce vehicle congestion on road segments. In addition, this model is also capable of avoiding segment choices that exceed road capacity, which is expected to reduce vehicle congestion. Therefore, the resulting QUBO model can be applied to QA engines to reduce congestion on road segments.
UAV Imagery-Based Potential Forest Fire Detection Using YOLOv10
Forest fire mitigation requires an early detection system that is both fast and reliable. This study presents a real-time potential forest fire detection system based on UAV imagery using the YOLOv10 object detection model. The main objective is to enhance the accuracy of detecting fire and smoke in aerial imagery and to minimize false alarms through hyperparameter optimization and data balancing strategies. The dataset used was compiled from Roboflow Universe and Kaggle, consisting of two object classes: fire and smoke, with a slight class imbalance (1329 fire and 1024 smoke). In total, 1,691 annotated images were used, covering various lighting conditions, smoke densities, camera angles, and geographic backgrounds, and were divided into training, validation, and test sets with a ratio of approximately 75:15:10. To address the class imbalance and visual variability, data augmentation techniques such as rotation, flipping, brightness adjustment, and noise addition were applied, along with loss weighting to improve learning performance for the minority smoke class. Model training was conducted using 24 hyperparameter configurations combining six optimizers, two batch sizes, and two learning rates. The best hyperparameters are NAdam optimizer, batch_size 24, and learning_rate 0.001.The best performance of accuracy, precision, recall, F1-score, mean IoU, and mAP were achieved at 0.879, 0.8705, 0.8575, 0.863, 0.7373, and 0.870, respectively. Real-time testing using a DJI Mini 4 Pro UAV with RTMP livestream input demonstrated stable and responsive detection, displaying bounding boxes, class labels, confidence scores, and a “POTENTIAL FOREST FIRE” indicator when both fire and smoke were detected simultaneously. These findings confirm that integrating UAV and YOLOv10 technologies provides an effective and adaptive approach for real-time early detection of potential forest fires.
Feature Engineering for Tropical Rainfall Forecasting Using Random Forest and Support Vector Regression
The complex dynamics of weather variability in Indonesia, influenced by multiple climatic drivers, make rainfall forecasting in tropical regions a significant scientific challenge. This study proposes an automated feature engineering pipeline to enhance the performance of Random Forest Regression (RFR) and Support Vector Regression (SVR) models for tropical rainfall prediction. Monthly rainfall data spanning 388 months (1993–2025) from a BMKG station were used as the basis for model development. The pipeline systematically generates temporal, seasonal, statistical, and anomaly-based features to provide domain-informed representations for non-sequential learning algorithms. Model performance was evaluated under four temporal data partitioning scenarios using R², RMSE, and probabilistic confidence intervals derived from bootstrap residual simulations. Results indicate that RFR achieved the highest predictive accuracy (R² = 0.93; RMSE = 31.01 mm) and demonstrated superior temporal–seasonal stability (Rolling CV: R² = 0.81 ± 0.07; RMSE = 55.44 ± 16.18), with comparable performance between wet and dry seasons. Conversely, SVR showed greater sensitivity to seasonal variability, with R² dropping to 0.55 during the wet season, indicating higher uncertainty under extreme rainfall conditions. Robustness and drift analyses further revealed that RFR adapts better to temporal and seasonal shifts, while SVR remains relevant as an adaptive model for extreme risk analysis. Overall, this study contributes to the development of automated feature engineering, reproducible climatological forecasting pipelines, and probabilistic modeling frameworks for rainfall prediction under uncertainty in tropical regions.
Spatial Analysis of Ensemble Learning Models for Agricultural Drought Early Warning
Drought poses a serious threat to rice production and local food security, triggered by climate anomalies such as El Niño. This study aims to evaluate and compare the performance of Ensemble Learning Models in classifying drought levels and analyze its correlation with periods of climate anomalies. This study uses Landsat 9 image data in the simulation period from June 2024 to July 2025, which is processed with HSV-based pan-sharpening and spectral index extraction (NDVI, NDWI, NDDI, EVI, LST). The modeling process applied undersampling to address class imbalance and hyperparameter tuning optimization using Optuna. The models compared included Random Forest, LightGBM, AdaBoost, XGBoost, and Gradient Boosting. The results showed that Gradient Boosting excelled with a train accuracy of 96,85% in original dataset with split dataset 70:30, whereas rise to 98.98% after tuning. Spatial validation was conducted in other rice field plots, however its steadfastly on research area with same treatment. The classification map shows the dominance of the moderate category, which temporally coincides with the period of rainfall decline associated with El Niño, although a direct causal relationship requires further investigation. These findings confirm that remote sensing combined with machine learning is effective for drought monitoring, with the caveat that the application of undersampling and limited spatial validation that is, confined solely to the research area; needs to be considered in the interpretation of results.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process
Cognitive Diagnosis Models (CDMs) in Computerized Adaptive Testing (CAT) are widely used to assess students’ cognitive abilities; however, existing approaches face significant limitations. The Latent Trait Model often suffers from specification errors due to its complexity, the Diagnostic Classification Model encounters difficulties in integrating hierarchical structures, and Deep Learning Models demand substantial computational resources. To address these challenges, this study introduces Salmon Salar Optimization (SSO) to enhance CDM performance and integrates the Partially Observable Markov Decision Process (POMDP) to improve dynamic question selection. The proposed adaptive testing framework comprises three components: preprocessing, CDM, and a selection algorithm. Experimental results on the ASSISTments 2009-2010 dataset demonstrate that SSO outperforms representative baselines from both deep learning: Neural CD and Latent Trait Model: MIRT approaches. Using 5-fold cross-validation, the proposed model achieved superior predictive performance with 75.51% accuracy and an AUC of 0.8191, highlighting its robustness compared to existing state-of-the-art methods. Furthermore, adaptive test simulations reveal that the SSO- and POMDP-based model delivers superior outcomes, attaining 80.3% accuracy with a reward of 8.03 for 10-question exams and 79.8% accuracy with a reward of 11.97 for 15-question exams. These findings confirm the effectiveness of the proposed model in enhancing cognitive diagnosis and adaptive testing performance.
Leveraging Generative AI in Vehicles for Enhanced Driver Safety and Advanced Communication Systems
This paper proposes an integrated artificial intelligence–based driver assistance system for electric vehicles (EVs) that combines computer vision–based drowsiness detection with a generative artificial intelligence (GenAI)–driven conversational interaction framework to enhance driver safety and human–vehicle interaction. The primary objective of this work is to reduce fatigue-related driving risks while enabling natural, hands-free, and context-aware communication between the driver and the vehicle. The core idea is to tightly couple real-time driver state monitoring with intelligent conversational feedback, allowing safety alerts and voice interactions to adapt dynamically to the driver’s condition. Driver drowsiness is detected using non-intrusive visual indicators, namely eye closure duration and blink rate, extracted from an in-vehicle camera. A drowsy state is identified when eye closure exceeds 10 s or when the blink rate exceeds 6 blinks within a 6 s interval. Upon detection, the system generates multi-modal alerts consisting of audio warnings and vibration feedback, while a GenAI-based natural language processing module provides real-time, hands-free voice interaction. Experimental evaluation was conducted on an ESP32-based embedded prototype across five predefined driving scenarios representing normal and fatigued conditions. The results show stable face and eye detection under normal driving and achieved 100% correct alert triggering in all drowsiness-related cases (3 out of 5 scenarios), with zero false positives observed during non-drowsy conditions (2 out of 5 scenarios). The system demonstrated consistent real-time response and reliable alert activation under fatigue conditions. The main contribution and novelty of this research lie in the real-time integration of generative AI–driven conversational intelligence with embedded computer vision–based drowsiness detection within a unified, resource-constrained platform, which is rarely addressed jointly in existing systems. Overall, the proposed framework provides a practical, scalable, and human-centered solution for intelligent driver assistance in semi-autonomous and future autonomous EV environments.
Dynamic IoT–PID Control for Energy-Efficient Water Distribution: EPANET-Based Digital Twin Validation in Varied Geographical Terrains
Topographical heterogeneity in water distribution networks frequently causes pressure imbalance, hydraulic inefficiency, and elevated energy consumption, particularly in regions with significant elevation gradients. This study aims to develop and validate a dynamic Internet of Things (IoT)-based pressure control model within a cyber–physical system framework for energy-efficient water distribution under varied geographical conditions. The primary contribution of this work lies in the separation of strategic and tactical control layers, where a Digital Twin based on EPANET dynamically generates optimal pressure setpoints, while distributed proportional–integral–derivative controllers execute real-time valve regulation at the network edge. The research adopts a Design Science Research methodology to design, implement, and evaluate a four-layer architecture consisting of physical sensing and actuation, long-range communication, tactical control, and strategic simulation layers. Validation is conducted using EPANET-based simulations across three control scenarios: a baseline condition without dynamic control, a static rule-based valve control scenario, and the proposed dynamic IoT–PID control scenario. The experimental procedure involves comparative analysis using control performance metrics including overshoot, settling time, steady-state error, and root mean square error. Simulation results demonstrate that the baseline configuration suffers from severe pressure imbalance and hydraulic backflow, while static rule-based control partially mitigates inefficiencies but fails to adapt to demand variability. In contrast, the proposed dynamic IoT–PID approach achieves precise pressure regulation with overshoot below 2% and tracking error maintained under 0.5 meters across all evaluated scenarios. These findings confirm that integrating a Digital Twin with real-time PID control significantly improves pressure stability and operational efficiency. The proposed architecture offers practical implications for smart water infrastructure in geographically diverse regions, providing a scalable foundation for adaptive pressure management, energy optimization, and future digital-twin-driven water distribution systems.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities
Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Global Air Quality Index Prediction Using Machine Learning on Major Pollutants
Air pollution remains a major global concern due to its significant impact on public health and environmental sustainability. This study aims to develop a reliable global Air Quality Index (AQI) prediction model by evaluating five regression-based machine learning algorithms, including Linear Regression, Support Vector Regression, Random Forest, XGBoost, and LightGBM. The dataset contains over twenty thousand pollutant concentration records from multiple countries. Since the dataset consists of independent pollutant observations without timestamps or temporal sequences, this research employs supervised regression techniques rather than time-series forecasting methods to ensure methodological consistency with the non-temporal structure of the data. The methodology includes data preprocessing, validation of geocoded country information for missing values, transformations to address skewed pollutant distributions, and feature selection based on established environmental standards. Sample weights were applied to account for uneven regional representation, and systematic hyperparameter tuning with cross-validation was conducted to optimize model parameters and reduce potential overfitting. Evaluation metrics are supported by correlation analysis to quantify relationships between pollutants and AQI. The results show that XGBoost delivers the highest and most stable performance, with a MAE of 0.0216, MSE of 0.0010, RMSE of 0.0318, R² of 0.9971, and MAPE of 0.5664. Feature importance analysis highlights PM2.5 as the most influential pollutant, followed by ozone, nitrogen dioxide, and carbon monoxide. The predicted AQI values closely align with observed measurements, demonstrating strong generalizability across regions. An interactive dashboard was developed to visualize AQI predictions and pollutant contributions across countries, improving practical usability for environmental monitoring. Overall, this study provides a comprehensive framework for global AQI prediction and demonstrates the potential of machine learning to support decision-making in environmental management and public health planning.
Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam
This study develops a reliable face-based verification system for online examinations by integrating a face recognition model with a blink detection mechanism to minimize the risk of identity fraud, also known as "contract cheating," and static image manipulation. "Contract cheating" refers to the practice where students hire others to complete their exams or assignments, compromising academic integrity. The growing reliance on online exams has raised concerns about the credibility of facial verification, as conventional methods are often vulnerable to spoofing attempts. To address this issue, the proposed system combines FaceNet, a deep learning model for identity recognition, with Dlib’s eye blink detection to provide a stronger layer of protection. The system was evaluated using 5-fold and 10-fold K-fold cross-validation, and additional testing assessed the impact of different video frame rates on performance. The results show that the system performs effectively in identifying legitimate users and detecting spoofing. FaceNet achieved an accuracy of 96.67 percent, outperforming DeepFace, which showed poorer results in precision, recall, and F1 score for some participants. Both models were evaluated on the same dataset, consisting of 150 images. The preprocessing pipeline, including face detection using MTCNN, cropping, and resizing, was applied consistently to both models to ensure a fair comparison of their performance. The system also demonstrated adaptability, achieving correct classifications at both 15 and 30 frames per second. Anti-spoofing tests based on the eye blink detection system detected all real faces, while static images were classified as spoofing. These results confirm that combining face recognition with liveness detection enhances the security of online examination platforms. The findings demonstrate the system's potential to reduce contract cheating and impersonation fraud, making online examinations more credible. Future work may focus on implementing adaptive thresholding for blink detection and integrating multimodal verification techniques to improve robustness across diverse real-world environments.