cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Husni Teja Sukmana
Contact Email
husni@bright-journal.org
Phone
+62895422720524
Journal Mail Official
jads@bright-journal.org
Editorial Address
Gedung FST UIN Jakarta, Jl. Lkr. Kampus UIN, Cemp. Putih, Kec. Ciputat Tim., Kota Tangerang Selatan, Banten 15412
Location
Kota adm. jakarta pusat,
Dki jakarta
INDONESIA
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 588 Documents
 55   56   57   58   59 
Nonintrusive Arrhythmia Detection from Wrist Pulse Using NTSC Color Model in Eulerian Video Magnification Baby Lolita Basyah; Hustinawaty Hustinawaty; Miftahul Jannah
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 Efori Bu'ulolo; Poltak Sihombing; Sutarman Sutarman; Mohammad Andri Budiman
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 Adi Suryaputra Paramita; Indra Maryati; Christian Christian; Elizabeth Nathania Witanto; Auezova Raya Tileubaevna; Choo Wou Onn
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.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
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.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Performance Evaluation of Support Vector Machine (SVM) and XGBoost for Predicting Toddlers’ Stunting Status Based on Anthropometric Data Nurjoko Nurjoko; Admi Syarif; Favorisen R. Lumbanraja; Khairunisa Berawi
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.v7i2.1260

Abstract

Stunting remains a primary global health concern, particularly in developing countries, due to its long-term effects on physical growth, cognitive development, and overall well-being. Despite various public health initiatives, challenges in early detection persist, highlighting the need for accurate, data-driven predictive models to support targeted interventions. This study aims to develop and compare the performance of two machine learning algorithms—SVM and Extreme Gradient Boosting (XGBoost)—for classifying stunting status among children under five, in order to determine the most effective method for early prediction. A quantitative machine learning approach was applied to a dataset comprising 17,498 records derived from Posyandu data in Lampung Province, Indonesia. The analytical pipeline included data preprocessing, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and model evaluation through stratified 10-fold cross-validation. Performance was assessed using accuracy, precision, recall, and F1-score. The XGBoost model demonstrated superior performance with accuracy, precision, recall, and F1-score reaching 0.9979. In comparison, the SVM model produced slightly lower yet still strong results, achieving an accuracy of 0.9949, with similarly consistent performance across other evaluation metrics. These findings indicate that XGBoost more effectively handles high-dimensional, imbalanced data and captures nonlinear patterns in the dataset. XGBoost was identified as the optimal method for stunting classification in this study, outperforming SVM across all evaluation metrics. These results support the integration of boosting-based models into early detection systems for child nutritional assessment. Future studies should incorporate additional environmental and socioeconomic variables and evaluate model applicability in a real-time community health setting.
Enhancing Subjective Career Success Among Private University Academics: The Roles of Perceived Organizational Support, Proactive Personality, and Work Engagement Nafiudin Nafiudin; Corry Yohana; Agus Wibowo
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.v7i2.1295

Abstract

This study aims to develop and empirically test a conceptual model explaining the determinants of subjective career success among academics in private universities in Indonesia. Drawing on Social Cognitive Career Theory (SCCT) and insights from the Job Demands–Resources (JD-R) theory, proactive personality is conceptualized as a personal resource representing person input and perceived organizational support as a contextual resource that influence career outcomes. In this framework, work engagement is proposed as a key motivational mechanism through which personal and organizational resources are translated into subjective career success. A quantitative research design was employed using a survey method administered to full-time academics at private higher education institutions across Indonesia. Data were collected from 278 respondents and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS version 4. The results indicate that proactive personality and perceived organizational support exert positive and significant effects on subjective career success. Furthermore, work engagement partially mediates the relationships between proactive personality and subjective career success, as well as between perceived organizational support and subjective career success. These findings suggest that the availability of personal and organizational resources plays a critical role in shaping academics’ subjective career experiences. From a theoretical perspective, this study extends the application of SCCT by integrating motivational insights from JD-R theory, demonstrating how personal resources (proactive personality) and contextual supports (perceived organizational support) influence career outcomes through work engagement as a psychological mechanism. Practically, the findings offer insights for private university management in designing faculty development policies that foster work engagement and enhance subjective career success.
Hybrid Machine Learning for Early Prediction of At-Risk Students with Imbalanced Data Esti Wijayanti; Widowati Widowati; Catur Edi Widodo
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.v7i2.1368

Abstract

The phenomenon of student dropout remains a major challenge for higher education institutions because it impacts academic performance and institutional reputation. Identification of students at risk of dropping out is often hampered by data imbalance, where the number of dropouts is far fewer than active students, so conventional prediction models tend to be biased towards the majority class. This study aims to develop an accurate and reliable prediction framework for students at risk of dropping out to detect at-risk students through a hybrid machine learning approach with data balancing techniques. The main contribution of this study is the integration of Support Vector Machine and Extreme Gradient Boosting in a stacked ensemble architecture supported by data balancing optimization techniques. The proposed model leverages the ability of Support Vector Machine to separate complex classification patterns, while Extreme Gradient Boosting improves prediction accuracy through iterative learning and modeling interactions between variables. The problem of data imbalance is addressed through oversampling techniques for the minority class so that the model learning process becomes more balanced. The model framework is tested using a dataset consisting of 3,652 students with academic, socioeconomic, and behavioral variables. Experimental results show that the proposed hybrid model outperforms the single model, with an accuracy rate of 97 percent, a precision rate of 94 percent, and a recall rate of 95 percent. These findings suggest that a combination of complementary machine learning methods, coupled with data optimization, can significantly improve the predictive ability of student dropout. The practical implication of this research is the availability of a robust decision support system for universities in designing timely and targeted interventions. By identifying students at risk of dropping out, institutions can strengthen retention strategies, improve student academic success, and reduce dropout rates more effectively.
Institutional Readiness and Digital Service Quality as Determinants of Entrepreneur Trust in Risk Based Business Licensing Services Tunggul Sihombing; Medlin Anggreyni Hura; Asima Yanty Siahaan
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.v7i2.1429

Abstract

This study examines digital licensing governance in risk-based business licensing services by integrating institutional readiness, digital service quality, service dependability, procedural transparency, public trust, entrepreneur satisfaction, and compliance intention. A quantitative explanatory design was applied using 300 valid responses from entrepreneurs who had used local government licensing services and the Online Single Submission system. The respondent profile shows that 73.0% were micro and small enterprises, 60.7% operated in medium- and high-risk categories, 50.4% used assisted online services at the DPMPTSP office, and 38.3% experienced permit processing durations of more than seven days. The measurement model demonstrated strong validity and reliability, with Composite Reliability values ranging from 0.908 to 0.936 and AVE values ranging from 0.663 to 0.701. The structural model revealed that institutional readiness significantly affected digital service quality and service dependability, while digital service quality strongly influenced service dependability and procedural transparency. Procedural transparency had the strongest direct effect on public trust, with a path coefficient of 0.547, followed by entrepreneur satisfaction toward compliance intention at 0.511 and public trust toward entrepreneur satisfaction at 0.452. Mediation analysis confirmed that digital service quality contributed to satisfaction through service dependability, procedural transparency, and public trust. The findings show that digital licensing reform does not depend solely on system availability, but on the alignment of institutional capacity, digital quality, reliable service delivery, transparent procedures, and public trust. This study contributes a comprehensive governance model for explaining how digital public services strengthen satisfaction and formal compliance among entrepreneurs.

Page 59 of 59 | Total Record : 588

 55   56   57   58   59 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol 7, No 2: May 2026 Vol 7, No 1: January 2026 Vol 6, No 4: December 2025 Vol 6, No 3: September 2025 Vol 6, No 2: MAY 2025 Vol 6, No 1: JANUARY 2025 Vol 5, No 4: DECEMBER 2024 Vol 5, No 3: SEPTEMBER 2024 Vol 5, No 2: MAY 2024 Vol 5, No 1: JANUARY 2024 Vol 4, No 4: DECEMBER 2023 Vol 4, No 3: SEPTEMBER 2023 Vol 4, No 2: MAY 2023 Vol 4, No 1: JANUARY 2023 Vol 3, No 4: DECEMBER 2022 Vol 3, No 3: SEPTEMBER 2022 Vol 3, No 2: MAY 2022 Vol 3, No 1: JANUARY 2022 Vol 2, No 4: DECEMBER 2021 Vol 2, No 3: SEPTEMBER 2021 Vol 2, No 2: MAY 2021 Vol 2, No 1: JANUARY 2021 Vol 1, No 2: DECEMBER 2020 Vol 1, No 1: SEPTEMBER 2020 More Issue