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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 583 Documents
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Nonintrusive Arrhythmia Detection from Wrist Pulse Using NTSC Color Model in Eulerian Video Magnification Basyah, Baby Lolita; Hustinawaty, Hustinawaty; Jannah, Miftahul
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1293

Abstract

Arrhythmia is a cardiovascular condition characterized by abnormal heart rhythms, such as tachycardia and bradycardia, which may lead to serious health complications if not detected early. This study proposes a non-invasive approach for screening tachycardia by extracting pulse signals from wrist video recordings using Eulerian Video Magnification (EVM) combined with the NTSC color space model. Subtle variations in skin color caused by blood flow, which are typically imperceptible to the human eye, are amplified using the EVM technique to enhance pulse-related motion signals. The NTSC color model is employed to separate luminance and chrominance components (YIQ), allowing more effective identification of pulse-induced color variations in the wrist region. The recorded wrist videos are processed through several stages, including spatial decomposition, temporal filtering, motion magnification, and pixel intensity extraction from the region of interest to obtain a temporal pulse signal. Peak detection is then applied to estimate heart rate in beats per minute (BPM). The performance of the proposed method is evaluated by comparing the estimated BPM values with reference measurements obtained from a Xiaomi Mi Band 2 wearable device. Experimental results based on 20 wrist video recordings demonstrate that the proposed method achieves approximately 96% agreement between the estimated BPM values and the reference measurements. Quantitative evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation analysis further confirms the consistency of the proposed approach. These results indicate that the integration of Eulerian Video Magnification with the NTSC color model has potential as a low-cost and non-contact method for preliminary tachycardia screening and remote cardiovascular monitoring.
K-Cube Consensus Clustering with Centroid Improvement and Variance-Based Metrics on High-Dimensional Data Bu'ulolo, Efori; Sihombing, Poltak; Sutarman, Sutarman; Budiman, Mohammad Andri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1209

Abstract

High-dimensional and multidimensional cube data structures (K-Cube) are posing a significant challenge for conventional clustering algorithms due to the effect of dimensionality, uniform feature weight assumptions, and loss of hierarchical information. Therefore, this study aimed to propose K-Cube Consensus Clustering framework, which integrates Variance-Based Centroid Refinement, Weighted Distance Metrics, and consensus voting mechanism to overcome the challenges of high-dimensional cube data. The proposed method systematically clustered all dimensions and sub-dimensions of cube data, refined centroid by emphasizing more stable low-variance attributes, and applied adaptive distance weighting based on variance-derived feature weights integrated into the distance metric to improve cluster assignment. The final clusters were obtained through majority voting of the clustering results for each dimension. Unlike existing consensus clustering methods that operate on flat data representations or combine independent clustering results, the proposed framework explicitly exploits the hierarchical structure of multidimensional cube data by clustering dimensions and sub-dimensions prior to consensus integration. Moreover, variance-based centroid refinement and weighted distance metrics are jointly embedded within each cube dimension rather than applied as isolated enhancements. This hierarchy-aware design preserves cube semantics while simultaneously improving centroid stability and distance adaptivity, resulting in a distinct and scalable clustering framework for complex high-dimensional cube data. The framework processes cube dimensions independently with iterative convergence control, enabling scalable application to large-scale cube data. The results of synthetic and real-world high-dimensional datasets, including cube data with approximately 2.2 million instances, showed that the proposed method consistently outperformed K-Means, K-Medoids, and Hamiltonian formulations. The method produced lower SSE such as 3,179,328 on Arcene and 1,422.21 on Lung Cancer, higher Silhouette Score of approximately 0.5718 and 0.4905 for consensus results, better cluster stability of 0.9947, and faster convergence. These results confirmed the effectiveness of K-Cube Consensus Clustering in producing stable and meaningful clusters in large-scale high-dimensional data applications.
Interpretable Temporal Risk Modeling for Contributor Inactivity Prediction: A Comparative Study of Tree-Based Ensembles Paramita, Adi Suryaputra; Maryati, Indra; Christian, Christian; Witanto, Elizabeth Nathania; Tileubaevna, Auezova Raya; Onn, Choo Wou
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1311

Abstract

This study aims to develop an interpretable temporal risk modeling framework for predicting contributor inactivity in collaborative development environments, thereby supporting sustained participation and improving productivity. The research focuses on contributor activity data collected from a collaborative software development platform, in which participation histories are represented by temporal engagement features that capture activity recency, participation intensity, and contribution patterns over time. To model inactivity risk, several tree-based ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and a stacking ensemble, are employed and evaluated under imbalanced classification conditions. Experimental results demonstrate strong predictive performance across models, with Random Forest achieving the highest AUC of 0.9401, while XGBoost obtains the best Matthews Correlation Coefficient (0.7353). The novelty of this study lies in prioritizing structured temporal behavioral representation through normalized temporal engagement features rather than increasing model complexity, enabling more interpretable inactivity risk modeling. The findings provide practical implications for collaborative platform managers by enabling early identification of contributor disengagement, supporting sustained participation, improving productivity, and facilitating continuous product innovation.

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