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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 7, No 1: January 2026" : 54 Documents clear
A Dual Pathway of Governmental Support for SME Performance: Paradoxical Impact of Organizational Learning Capabilities Do, Ngoc Bich; Nguyen, Hue Minh; Do, Hai-Ninh; Nguyen, Kim Thao; Nguyen, Tra My
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1091

Abstract

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.
Spatial Analysis of Ensemble Learning Models for Agricultural Drought Early Warning Sudianto, Sudianto; Ni'amah, Khoirun; Dewi, Atika Ratna; Ramadhan, Afan; Aprilia, Jeti; Tiyaswening, Arsita Wiwit; Anataya, Syalaisha Nisrina
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1108

Abstract

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.
Global Air Quality Index Prediction Using Machine Learning on Major Pollutants Santoso, Richard; Iskandar, Karto
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1112

Abstract

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.
A Practical YOLO Approach to Classifying Thai Freshwater Snails of Economic Significance Nuankaew, Wongpanya S.; Aunban, Jirasak; Kansuree, Thanapoom; Nuankaew, Kuljira S.; Nuankaew, Kaewpanya S.; NUANKAEW, Pratya
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1099

Abstract

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.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Saputro, Rujianto Eko; Utomo, Fandy Setyo; Wanti, Linda Perdana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1065

Abstract

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.
Lightweight Brain Tumor Classification with Histogram Oriented Gradients (HOG) Features and Class-Weighted Support Vector Machine (SVM) Warsito, Budi; Fadhilah, Husni; Kartikasari, Puspita; Hakim, Arief Rachman
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1018

Abstract

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.
Quantum-Inspired Optimization for Traffic Congestion: A QUBO-Based Approach with Simulated Annealing Tambunan, Toufan Diansyah; Suksmono, Andriyan Bayu; Edward, Ian Joseph Matheus; Mulyawan, Rahmat
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.905

Abstract

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.
Leveraging Generative AI in Vehicles for Enhanced Driver Safety and Advanced Communication Systems P, Vinoth Kumar; T, Sri Anadha Ganesh; Batumalay, M; Kumar, S N; Devarajan, Gunapriya; K, Bhuvaneshwari; T, Kesavan; S, Lakshmi Praba; S, Nandhanaa K
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.809

Abstract

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.
Financial Inclusion as a Pathway to Sustainable Development: Regional Evidence from ASEAN and the Case of Vietnam Khoi, Nguyen Tien; Dinh, Le Quoc
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1027

Abstract

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.
UAV Imagery-Based Potential Forest Fire Detection Using YOLOv10 Pardede, Jasman; Pratama, Muhamad Rifki; Milenio, Rizka Milandga
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1052

Abstract

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.

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