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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 518 Documents
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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.
A Data-Driven MINLP Approach for Enhancing Sustainability in Blockchain-Enabled e-Supply Chains Badawi, Afif; Efendi, Syahril; Tulus, Tulus; Mawengkang, Herman
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.v6i4.889

Abstract

Modern e-supply chains are characterized by increasing complexity and a critical need for enhanced sustainability, transparency, and traceability. Blockchain technology emerges as a significant enabler, offering decentralized, immutable ledgers and smart contracts that can support more secure, verifiable, and environmentally responsible operations through trustworthy data. Despite blockchain's potential, a notable gap exists in the availability of quantitative, data-driven optimization models that rigorously assess the operational and sustainability impacts of its integration into e-supply chains, particularly for complex, non-linear system interactions. This study aims to address this gap by presenting an in-depth analysis of a specific Mixed-Integer Non-Linear Programming (MINLP) optimization model. The goal is to clarify its structure, evaluate its application for an e-supply chain incorporating blockchain features (like transaction costs and conceptual smart contract enforcement for compliance) and sustainability objectives (such as carbon emission reduction), and derive practical insights. The methodology involves a detailed exposition of the MINLP model, followed by its application to a defined e-supply chain scenario. The analytical approach includes computational experiments focusing on a base case analysis to demonstrate model functionality. The broader evaluative framework for this study encompasses benchmarking the model’s performance against a conventional system and conducting sensitivity analyses on key parameters to understand performance trade-offs. The initial base case analysis demonstrates the model's capability to optimize supplier selection and operational plans while adhering to sustainability constraints, such as maintaining carbon emissions at or below 300 kg CO₂ per period, and accounting for blockchain-specific costs like a per-supplier usage fee of 500. The structure of the model and preliminary insights suggest its potential to achieve improved environmental impact compared to conventional systems, balanced against associated blockchain implementation costs. This research provides a detailed examination of a complex MINLP structure, offering a data-driven analytical approach for assessing blockchain's role in sustainable e-supply chains. It furnishes a foundational framework and insights that can guide managerial decisions and strategic planning for industries transitioning towards greener, more transparent, and digitally advanced supply chain operations.
Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas Devis, Yesica; Muhamadiah, Muhamadiah; Yulanda, Yulanda; Irawan, Yuda; Wahyuni, Refni
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.v6i4.898

Abstract

Dengue fever is an endemic disease that poses a serious threat to public health in tropical regions such as Indonesia. Efforts to control this disease require a data-based approach that is able to accurately predict the level of risk so that interventions can be targeted. This study aims to develop a predictive model of DHF risk using ensemble stacking method optimized with Optuna algorithm and integrated into an interactive dashboard based on Streamlit. The dataset used includes environmental, climate, and socio-demographic indicators from 2015 to 2024 with a total of 1,440 data entries. The preprocessing process includes normalization with Standard Scaler, feature selection using LASSO, and label data balancing with the SMOTE method. Model validation was performed using 10-Fold Cross Validation to ensure model generalization to new data. The stacking model is built with three basic algorithms, namely SVM, KNN, and Random Forest, which are combined using Logistic Regression as a meta-learner. The evaluation results show that the model is able to achieve an average accuracy of 97.57%, with high precision, recall, and f1-score values in all three prediction classes (low, medium, high). The ROC-AUC for each class also showed near-perfect performance. The implementation of the model in the Streamlit dashboard allows non-technical users such as health center or health office staff to perform regional risk prediction and obtain data-driven intervention recommendations automatically. This research not only contributes to the development of predictive technology, but also strengthens evidence-based health promotion practices in urban areas. Further research is recommended to integrate IoT-based real-time data and expand the scope of application areas.
A Hybrid GRG-Neighborhood Search Model for Dynamic Multi-Depot Vehicle Routing in Disaster Logistics Hartama, Dedy; Poningsih, Poningsih; Tanti, Lili
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.v6i4.973

Abstract

In disaster relief logistics, timely and adaptive routing is critical to meet fluctuating demands and disrupted infrastructure. This paper proposes a Hybrid GRG–Neighbourhood Search (NS) model for solving the Multi-Depot Vehicle Routing Problem with Capacity and Time Dependency (MDVRP-CTD). The model integrates the Generalized Reduced Gradient (GRG) method for handling nonlinear capacity constraints and NS for local route refinement. The objective is to minimize total travel distance, delay penalties, and maximize vehicle utilization under dynamic disaster scenarios. Tested using the SVRPBench dataset, the hybrid model achieved up to 96.5% demand fulfillment, an 11% improvement in vehicle utilization, and a reduction in total distance by 7%, outperforming Tabu Search and ALNS in three simulation scenarios. The model demonstrates enhanced adaptability and responsiveness to time-sensitive, capacity-constrained environments. Its novelty lies in the integration of nonlinear optimization with adaptive local improvement tailored for disaster contexts, providing a robust decision-support tool for real-time humanitarian logistics.
Modernizing Medicinal Plant Recognition: A Deep Learning Perspective with Data Augmentation and Hybrid Learning Nagrath, Preeti; Batumalay, Malathy; Saluja, Dhruv; Kukreja, Harsh; Tegwal, Devanshi; Saini, Akash
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.v6i4.795

Abstract

This study proposes a deep learning-based solution to address the longstanding challenge of accurately identifying Indian medicinal plants, which are vital to Ayurvedic pharmaceutics but often misidentified due to their morphological similarities. The objective is to develop a reliable, automated classification system using image processing and advanced neural network architectures. A dataset of 5,945 images representing 40 distinct medicinal plant species was sourced from Kaggle and augmented to 11,890 images using techniques such as flipping, rotation, and scaling to enhance diversity. The models tested include a baseline Convolutional Neural Network (CNN), transfer learning with DenseNet121, DenseNet169, and DenseNet201, a voting ensemble of these DenseNet variants, and a hybrid DenseNet201-LSTM architecture. Experimental results show that the CNN model achieved the lowest accuracy at 69.58%, while the hybrid DenseNet201-LSTM model reached the highest validation accuracy of 93.38%, with a precision of 94.74%, recall of 93.38%, and F1-score of 93.42%. These findings confirm the hybrid model’s superior ability to capture spatial and sequential dependencies in leaf features. The novelty of this work lies in the integration of DenseNet201 with LSTM for medicinal plant classification, which has not been widely explored in this domain. The study also acknowledges dataset scalability as a limitation and proposes future work involving dataset expansion through botanical collaborations, integration of environmental metadata, and deployment of a mobile application using TensorFlow Lite for real-time, low-resource implementation. Overall, the research contributes a robust and scalable framework for medicinal plant identification, promoting trust in traditional medicine, supporting conservation efforts, and enabling practical field-level applications in both rural and clinical settings.
Enhancing U-Net for Wrist Fracture Segmentation in X-ray Images using Adaptive Callbacks and Weighted Loss Functions Radillah, Teuku; Defit, Sarjon; Nurcahyo, Gunadi Widi
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.v6i4.952

Abstract

The detection of wrist fracture through medical imaging is causing considerable challenges due to the subtle and variable manifestation of such ruptures, necessitating precise and reliable segmentation methods. Therefore, this research aimed to propose an improved U-Net model for detecting wrist fracture. The model incorporated two innovations, namely adaptive callback training and weighted loss combination. The adaptive callback mechanism could be performed by dynamically adjusting the training parameters based on the model performance to prevent overfitting and accelerate convergence. At the same time, the loss function combined Dice Loss and Binary Cross-Entropy (BCE) Loss with linear as well as non-linear exponential weighting strategies, ensuring balanced optimization between region-based accuracy and pixel classification. During this analysis, a series of experiments were conducted on a curated wrist X-ray image dataset, and the results showed that the proposed method expressed superior performance in terms of segmentation accuracy when compared with previous U-Net and other state-of-the-art procedures. The proposed method achieved 91% accuracy, 87% precision, 86% recall, and 87% F1 score. Following this discussion, the findings showed the efficacy of the adaptive training design and loss function in improving the strength and sensitivity of the model in detecting wrist fracture
AI-Driven Mobile Application for Self-Monitoring Personalized Premenstrual Symptoms and Risk Assessment of Depressive Crises in Female University Students Nuankaew, Pratya; Sorat, Jidapa; Intajak, Jindaporn; Intajak, Jirapron; Nuankaew, Wongpanya S.
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.v6i4.881

Abstract

Premenstrual Syndrome (PMS) and depressive symptoms are common concerns for female university students, often triggered by hormonal fluctuations before menstruation. These conditions can severely impact academic performance, interpersonal relationships, and overall well-being, particularly when symptoms escalate into severe depressive episodes. Even though the prevalence, awareness, and self-management strategies among students are on the rise, they remain limited, particularly in cultural contexts where women's health and emotional well-being receive little attention. This study presents the development of an AI-driven mobile application designed to facilitate personalized tracking of premenstrual symptoms and assess the risk of depressive episodes. The application integrates machine learning models trained on self-reported psychological and physiological data, using validated instruments such as DASS-21 and PSST-A. The research adopted a mixed-methods approach, involving survey-based symptom identification, model training and validation, system design, and user satisfaction evaluation. This research contributes to the development of artificial intelligence-assisted self-care technology for the purpose of monitoring personal health and taking preventative psychological measures. The findings indicate that the application that was developed is beneficial in terms of forecasting the likelihood of someone suffering from depression and fostering self-awareness regarding mental health among college students. Considering this, the system has the potential to develop into a useful tool for providing aid to female students attending universities.
Unsupervised Neural Networks for Breast Cancer Clustering: A Comparative Study of RBMs and SOMs with Interpretability Metrics Soundes, Mekki; Ahlam, Labdaoui
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.v6i4.860

Abstract

This study presents a comparative analysis of two unsupervised neural network models—Restricted Boltzmann Machines (RBMs) and Self-Organizing Maps (SOMs)—applied to breast cancer data clustering. The primary objective is to evaluate and benchmark these models in terms of their latent feature extraction, clustering accuracy, and interpretability in a medical diagnostic context. Using a preprocessed breast cancer dataset comprising 569 patient records and 30 clinical features, the models were trained and evaluated based on two internal clustering metrics: Silhouette Score and Davies-Bouldin Index (DBI). The proposed methodology, implemented in Python, emphasizes reproducibility and diagnostic relevance. RBMs achieved a Silhouette Score of 0.88 and a DBI of 0.52, indicating compact and well-separated clusters, while SOMs recorded significantly lower performance with a Silhouette Score of 0.34 and a DBI of 1.47. Furthermore, classification performance (based on cluster-label mapping) shows RBMs yielding precision between 0.82 and 0.92, and recall between 0.87 and 0.89 for benign and malignant cases. SOMs, although less accurate, offer superior visualization of high-dimensional data, which aids in exploratory analysis and interpretability. The key contribution of this work lies in the development of a standardized evaluation framework for unsupervised neural clustering in healthcare, combining quantitative clustering metrics with qualitative insights into clinical applicability. The findings demonstrate that RBMs are better suited for diagnostic tasks requiring high pattern recognition, whereas SOMs retain value for data exploration and decision explanation. This research introduces a novel integration of RBM-based clustering into medical analytics, highlighting its potential in supporting decision-making processes in oncology. Future work will extend this approach to hybrid models and multi-modal datasets, aiming to balance performance and explainability in complex diagnostic environments.

Page 44 of 52 | Total Record : 518

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