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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 54 Documents
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Search results for , issue "Vol 6, No 4: December 2025" : 54 Documents clear
YOLOv12 Model Optimization for Monitoring Occupational Health and Safety in Hospital Archive Rooms Jepisah, Doni; Octaria, Haryani; Muhamadiah, Muhamadiah; Irawan, Yuda
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.936

Abstract

The application of artificial intelligence technology in occupational safety monitoring systems within healthcare facilities has become an urgent necessity, particularly to support compliance with Occupational Safety and Health (OSH) standards in hospitals. This study aims to develop an automated detection model based on YOLOv12 to identify visual OSH elements in hospital archive rooms, such as APAR, evacuation signs, windows, and Personal Protective Equipment (PPE) including masks, gloves, and shoes. The initial dataset consisted of 2,866 documented images, which were expanded through augmentation to 6,886 images to increase data diversity and prevent overfitting. The YOLOv12 model was trained over 100 epochs using SGD as the optimization technique. The dataset was divided into three subsets training, validation, and testing in a proportional manner. Model evaluation employed metrics such as precision, recall, mAP@0.5, and mAP@0.5–0.95, supported by visualizations including the confusion matrix, F1-confidence curve, and precision-recall curve. One of the key advantages of YOLOv12 lies in its architectural efficiency and enhanced generalization capability, enabled by the integration of R-ELAN, Area Attention Mechanism, and FlashAttention. These components allow for broader receptive field processing with reduced computational complexity. Furthermore, the removal of positional encoding and adjustment of the MLP ratio make the model lighter and faster without compromising accuracy. Compared to previous versions (YOLOv8–YOLOv11), YOLOv12 demonstrates more stable and accurate performance in detecting complex OSH objects in indoor environments. The system was also implemented in a real-time user interface using Streamlit, automatically displaying personnel PPE completeness and room safety compliance status. In conclusion, the optimized YOLOv12 model has proven effective for real-time visual detection in OSH contexts. Future studies are recommended to incorporate data balancing approaches, spatial segmentation, and IoT sensor integration to expand the system’s coverage and resilience across diverse workplace conditions.
Moodle-based Blended Learning: Factors Influencing the Behavioral Intention of Undergraduate Students Khazalah, Fayez; Alrababah, Saif Addeen; Mansour, Ayman; Alafif, Tarik
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.888

Abstract

The demand for blended learning by higher education has increased since COVID-19. Blended learning combines the advantages of both face-to-face and online learning. Many HEIs in developing countries have started to depend on Moodle to offer blended courses to their students, as it is freely available and open source. The current study aims to explore the factors that influence the intentions to use Moodle-based Blended Learning (MBBL) by higher education students in a public university in Jordan, a developing country. For this purpose, we used a modified version of the UTAUT2 model. Data were gathered through a survey that targeted undergraduate students. The study used 319 valid response samples and analyzed the data using SmartPLS 4 software that implements PLS-SEM analysis. The data analysis results show that the factors that influence the students’ behavioral intention to use MBBL are performance expectancy (β = .18), effort expectancy (β = .21), social influence (β = .16), and habit (β = .25). However, the results indicate that facilitating conditions and hedonic motivation factors do not have a significant influence. In addition, the results reveal that result demonstrability has significant effect on both performance expectancy (β = .58) and effort expectancy (β = .52). Also, effort expectancy is found to influence performance expectancy (β = .17). Among the influential factors, habit is identified as the strongest predictor of intentions followed by effort expectancy, whereas social influence is the weakest predictor. The proposed model was able to explain 50% of variance in students’ intentions to use MBBL. The current study provides HEIs with valuable insights needed to improve the MBBL process and enhance the performance of students. It also suggests future research directions that build on this study to reach more generalized and stable results.
Digital Platform Utilization and ICT Literacy on Global Market Access among MSMEs: The Mediating Role of Digital Business Readiness and the Moderating Effect of Government Support Nurfaizal, Yusmedi; Kurniawan, Arief Adhy; Hermawan, Hellik; Surya Saputra, Dhanar Intan; Amalina, Siti Nahla; Hafshah, Luqyana Nida
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.893

Abstract

This study investigates how digital capabilities and institutional support shape global market access among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, with a specific focus on Banyumas Regency. The research integrates technology adoption, organizational readiness, and policy support into a unified structural model to explain MSME internationalization in semi-urban contexts. The objective is to assess the direct effects of digital platform utilization and ICT literacy on global market access, the mediating role of digital business readiness, and the moderating effect of government support. A survey of 125 digitally engaged MSMEs was analyzed using Partial Least Squares Structural Equation Modeling. The findings reveal that digital platform utilization (β = 0.163; p = 0.008) and ICT literacy (β = 0.161; p = 0.004) significantly and directly enhance global market access. Both constructs also positively influence digital business readiness digital platform utilization (β = 0.600; p 0.001) and ICT literacy (β = 0.497; p 0.001) which itself contributes to market access (β = 0.154; p = 0.037). Mediation analysis confirms that digital business readiness significantly mediates the relationship between digital capabilities and global market access, with indirect effects of 0.093 (p = 0.045) and 0.077 (p = 0.035) for digital platform utilization and ICT literacy, respectively. Furthermore, government support significantly moderates the effect of digital readiness on market access (β = 0.222; p = 0.002). The model demonstrates strong explanatory power (R² = 0.782 for global market access) and predictive relevance (Q² = 0.507). This study contributes to the digital transformation literature by positioning digital business readiness as a critical enabler of MSME internationalization and highlights the synergistic role of government interventions in amplifying internal digital capabilities. The novelty lies in applying an integrated model to a semi-urban developing economy setting, offering insights for inclusive digital ecosystem design and policy formulation.
Explaining Students' Digital Entrepreneurial Behavior: The Role of Social Media Adoption in an Integrated TPB–UTAUT Model Lestari, Elissa Dwi; Kurniasari, Florentina; Natania, Davina; Kurniawan, Alvin Yuan; Budiyanto, Hendro
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.965

Abstract

Amidst digital transformation and demographic bonuses in Indonesia, the emergence of digital entrepreneurship among the younger generation has become a promising yet challenging phenomenon. The main objective of this study is to develop and empirically evaluate an integrated model that explains students' digital entrepreneurial behavior by integrating psychological and technological viewpoints and combining the Theory of Planned Behavior (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT) approaches. TPB has been widely used to predict entrepreneurial intentions and behavior. However, TPB is not yet considered to be able to capture the role of comprehensive technology adoption in the context of digital entrepreneurship. To bridge this gap, this study integrates the UTAUT approach, which focuses on technology acceptance factors. This integration addresses the shortcoming of the TPB by completely including the impact of digital technology adoption on entrepreneurship, while the UTAUT fails to include psychological motivation. PLS-SEM analyzed data from 322 student entrepreneurs who run social media-based enterprises. The study found that the TPB-UTAUT framework explains 62.2% of the variation in social media adoption (R² = 0.622) and 62.6% of the variance in entrepreneurial activity (R² = 0.626). Eight out of nine hypotheses were supported: attitudes (β = 0.330, p 0.001) and perceived behavioral control (β = 0.189, p = 0.008) significantly influenced social media adoption, while attitudes (β = 0.155, p = 0.006), perceived behavioral control (β = 0.295, p 0.001), performance expectancy (β = 0.149, p = 0.011), and social media adoption (β = 0.225, p = 0.001) directly enhanced entrepreneurial behavior. Effort expectation influenced adoption (β = 0.183, p = 0.005) but not behavior (β = 0.101, p = 0.069). The novelty of this study lies in demonstrating that among digital-native students, effort expectancy loses significance in predicting entrepreneurial behavior, indicating a generational shift in technology adoption dynamics. These insights offer theoretical enrichment and practical implications for designing digital entrepreneurship curricula and policies in developing countries.
Clustering Cadet Training Performance Using K-Means and Ward's Method Evidence from OTMon Maritime Monitoring System Wahdiana, Dian; Ekohariadi, Ekohariadi; Suhartini, Ratna
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.958

Abstract

This study investigates cadet performance segmentation during on-board maritime training using clustering analysis of data from the On Training Monitoring (OTMon) system. Grounded in the competency-based education framework and experiential learning theory, the research aims to identify behavioral patterns and competency levels among 80 maritime cadets over a twelve-month sea-based training program. The OTMon application continuously recorded task completion rates, feedback interactions, sign-on consistency, and report submissions. K-Means clustering and Principal Component Analysis (PCA) revealed three distinct cadet profiles: Cluster 1 (high-performing) with average task completion of 92.4% and feedback frequency of 15.2 times/month; Cluster 2 (administratively consistent) with 88.1% completion but only 6.3 feedback interactions/month; and Cluster 3 (at-risk) with 67.5% completion and 3.8 feedback interactions/month. Linear Discriminant Analysis (LDA) validated the clusters with 98.8% resubstitution accuracy and 97.6% cross-validation accuracy, supported by generalized squared distances above 9.5 between all cluster pairs, indicating strong separation. These findings demonstrate that unsupervised clustering can reliably distinguish high-performing cadets from those needing targeted intervention, enabling data-informed mentoring and adaptive learning strategies in maritime education. The contribution of this study lies in integrating digital monitoring data with both unsupervised and supervised machine learning methods to enhance competency assessment. The novelty is in applying maritime-specific learning analytics for real-time performance segmentation, offering a scalable diagnostic framework for improving supervision quality and supporting individualized cadet development in vocational training contexts.
Stacking Ensemble with SMOTE for Robust Agricultural Commodity Price Prediction under Imbalanced Data Siagian, Yessica; Hutahaean, Jeperson; Mulyani, Neni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.916

Abstract

The volatility of agricultural commodity prices presents a substantial obstacle in the agribusiness sector, especially in supporting timely and data-driven decision-making. This volatility is primarily caused by the imbalanced distribution of historical price data and the complex, often nonlinear nature of price patterns. To address this challenge, this study proposes a novel predictive modeling approach by integrating Stacking Ensemble Learning and Synthetic Minority Over-sampling Technique (SMOTE). The dataset used in this research consists of 5,558 records and 9 features, sourced from a publicly available Kaggle dataset. The target variable daily price was transformed into three classes: low, medium, and high, using a quartile-based discretization approach to enable multiclass classification. The main objective is to evaluate whether stacking combined with SMOTE can improve model performance compared to baseline models that use individual algorithms. A total of eight models were constructed and compared: four baseline models using SMOTE only, and four stacking models integrating SMOTE. The experimental results demonstrate that the proposed model Decision Tree Regression with Stacking and SMOTE achieved the highest performance, with 98.68% accuracy, an F1-score of 0.9868, Cohen’s Kappa of 0.9803, MCC of 0.9803, ROC-AUC of 0.9995, and a log loss of 0.0529. Other optimized models also performed well, such as Random Forest (98.37% accuracy) and Gradient Boosting (98.56%). In contrast, baseline models such as Linear Regression and Decision Tree without stacking achieved only around 67–68% accuracy, with log loss exceeding 0.97. The key contribution of this study is the empirical evidence that combining stacking and SMOTE significantly enhances classification accuracy and model robustness in imbalanced datasets. The novelty lies in applying a deep learning-optimized stacking framework specifically for agricultural commodity price classification, along with a comprehensive multiclass evaluation, offering new insights for practical implementation in agricultural decision support systems.
Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models Naizabayeva, Lyazat; Sembina, Gulbakyt; Aliman, Alibek; Satymbekov, Maxatbek; Barlykbay, Nazym; Seilova, Nurgul
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.821

Abstract

This study develops an advanced forecasting methodology for air pollution levels in Almaty, Kazakhstan, focusing on fine Particulate Matter (PM2.5) and carbon monoxide concentrations. Air pollution poses significant risks to public health, and Almaty’s basin location exacerbates the problem. Addressing the limitations of traditional statistical forecasting methods, we propose an ensemble machine learning approach that integrates Seasonal-Trend decomposition with gradient boosting algorithms to capture complex temporal and nonlinear patterns. The objective is to develop and validate an effective methodology for forecasting atmospheric air pollution in Almaty using machine learning methods, in particular STL decomposition, XGBoost, LightGBM models, and their ensemble combination. The novelty lies in the integration of STL decomposition with an ensemble of gradient boosting models for high-accuracy air pollution forecasting in the complex urban environment of Almaty. The dataset includes hourly measurements from over 20 monitoring stations, enabling seasonal and spatial analysis. Rigorous preprocessing techniques were applied, including outlier removal, normalization, and time series decomposition into seasonal, trend, and residual components. Two gradient boosting models, XGBoost and LightGBM, were trained separately and combined into a weighted ensemble, with optimal weights determined through cross-validation. Figures and tables illustrate data preprocessing flow, model architectures, feature importance analysis, and evaluation of predictive performance. The ensemble outperformed individual models, achieving high accuracy with coefficient of determination values exceeding 0.98 for PM2.5 and 0.83 for carbon monoxide. The findings demonstrate that integrating Seasonal-Trend decomposition with ensemble learning provides a robust and effective approach to forecasting air pollution in complex urban environments. The methodology shows strong potential for practical application in real-time air quality monitoring and warning systems, aiding policymakers and public health authorities. Future research will expand the dataset by incorporating additional factors such as traffic flow, industrial emissions, and satellite remote sensing data to enhance predictive accuracy and model interpretability.
Development of a Smart Lung Health Monitoring System Using Sensors and Data Analytics for Early Disease Detection Tyulepberdinova, Gulnur; Kunelbayev, Murat; Amirkhanova, Gulshat; Sakypbekova, Meruyert; Adilzhanova, Saltanat; Tolepberdinova, Ardak
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.857

Abstract

This study introduces a novel multimodal wearable sensor system for real-time monitoring and analysis of respiratory and cardiac activity. The primary objective is to facilitate the early detection of cardiopulmonary abnormalities by integrating electrical (ECG) and acoustic data. A total of 30 participants, aged 25 to 50 years, were involved in controlled breathing experiments, which included deep (1000 ml, 15 breaths/min), moderate (750 ml, 20 breaths/min), and shallow (500 ml, 30 breaths/min) breathing, as well as coughing simulations. Signal processing using a 7th-order polynomial approximation yielded the lowest modeling error at 6.8%, ensuring precise waveform reconstruction. The system demonstrated a clear differentiation of respiratory patterns via Area Under the Curve (AUC) metrics, with average AUC values increasing from 1200 µV·s during shallow breathing to 3200 µV·s during deep breathing. Further analysis of the first derivative of AUC values revealed a strong correlation (r = 0.89) between respiratory volume and ECG amplitude fluctuations, highlighting robust cardiorespiratory coupling. Notably, the system achieved a 92% accuracy in detecting abnormal breathing events, such as shallow breathing and coughing fits. By combining ECG-derived heart rate variability with respiratory data, the system offers a comprehensive assessment of cardiopulmonary interaction. The key contribution of this work lies in its real-time, continuous monitoring capability using a compact wearable form factor, which distinguishes it from existing single-modality systems. This approach represents a significant advancement in non-invasive health monitoring, with strong potential for application in clinical diagnostics and home-based tracking of chronic conditions, such as asthma, COPD, and cardiac dysregulation.
Toward Improving Knee MRI Image Quality for Single Image Super-resolution Mediani, Hafssa; Tayeb, Salma; Mekouar, Soufiana; Himmi, Mohammed Majid
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.926

Abstract

This study aims to improve image super-resolution techniques by balancing distortion reduction with perceptual quality improvement. It introduces a new framework called Toward Improving Super-Resolution, which focuses on producing high-quality knee magnetic resonance images. The framework uses a lightweight, resolution-independent, feedforward convolutional network with 266,000 parameters, which includes smoothing and denoising preprocessing and Leaky Rectified Linear Unit activations for stable training. The model builds on a baseline deep learning architecture to improve training stability and visual quality while maintaining computational efficiency. The fast Magnetic Resonance Imaging knee dataset was compared to established super-resolution methods like Super-Resolution Convolutional Neural Network, Very Deep Super-Resolution, and Enhanced Deep Super-Resolution. Toward Improving Super-Resolution achieved a peak signal-to-noise ratio of 38.405 ± 0.129 decibels and a structural similarity index of 0.9815 ± 0.0021, surpassing Super-Resolution Convolutional Neural Network, Very Deep Super-Resolution, and Enhanced Deep Super-Resolution. It maintained high performance at scales three and four, demonstrating accuracy and statistical robustness. The study shows that the proposed framework can enhance diagnostic imaging, reduce the need for repeated scans, and speed up clinical decision-making.
SAGOMECON: An Adaptive ε-Constraint-Based Optimization Method for Multi-Criteria Decision-Making in Collaborative Industrial Networks Mesran, M; Sihombing, Poltak; Efendi, Syahril; Zarlis, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.941

Abstract

In collaborative industrial networks, decision-making processes often involve conflicting objectives such as minimizing operational costs and risks, while simultaneously maximizing efficiency and inter-organizational collaboration. Existing multi-objective optimization methods, including the ε-constraint and its variants, face significant challenges in handling dynamic constraints and achieving computational efficiency in real-world scenarios. To address these limitations, this study introduces SAGOMECON (Simplified Adaptive Optimization with Modified ε-Constraint), an enhanced optimization approach designed to support adaptive and efficient multi-criteria decision-making in dynamic environments. SAGOMECON extends the conventional SAUGMECON framework by incorporating real-time constraint updates, adaptive slack handling, and iterative refinement mechanisms, enabling it to maintain solution feasibility under shifting priorities and evolving operational conditions. The proposed method was evaluated using simulated datasets representing partner selection scenarios in collaborative networked organizations (CNOs). Comparative analysis against the traditional ε-constraint and SAUGMECON methods demonstrates that SAGOMECON consistently delivers Pareto-optimal solutions with reduced computational time and superior adaptability to dynamic changes. The findings suggest that SAGOMECON offers a practical and scalable solution for decision-makers in collaborative industrial settings, particularly where trade-offs between competing objectives must be navigated under uncertainty. This contribution is significant for industries seeking intelligent optimization strategies that align with agile and data-driven decision-making frameworks.

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