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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 55 Documents
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Search results for , issue "Vol 6, No 4: December 2025" : 55 Documents clear
Understanding Teacher Retention through the Lens of Job Satisfaction: An Empirical Study of Organizational and Human Resource Management in Chinese Universities Hui, Xie Xiao; Tresirichod, Teetut
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.v6i4.996

Abstract

This study investigates the determinants of teacher retention in public universities in Sichuan Province, China, emphasizing the mediating role of job satisfaction between organizational management (OM) and human resource management (HRM) on job retention (JR). The research aims to (1) identify the key factors influencing the retention of non-established teachers and (2) propose effective strategies to enhance their job stability. A quantitative approach was applied using a questionnaire survey administered to 1,400 teachers from 27 public universities, yielding 1,335 valid responses (95% response rate). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that both OM (β = 0.031, p 0.001) and HRM (β = 0.029, p 0.001) significantly and positively affect JR, while job satisfaction fully mediates these relationships (β = 0.030, p 0.001). The explanatory power of the model was moderate, with R² = 0.630 for job satisfaction and R² = 0.545 for job retention, indicating that the proposed model accounts for over 50% of the variance in both variables. Descriptive statistics further revealed that 50.3% of respondents were non-established teachers, and 61.5% were female, highlighting a balanced and diverse sample. The findings indicate that well-structured OM and HRM practices, such as transparent promotion systems, career development opportunities, and inclusive institutional participation, substantially enhance teacher satisfaction and retention. This study’s novelty lies in its integrated model combining OM, HRM, and job satisfaction to explain teacher retention, a topic rarely explored in the context of Chinese public universities. The research contributes to the literature by offering empirical evidence and actionable recommendations for policymakers and administrators to strengthen human resource strategies and ensure the long-term stability of university faculty.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya, Adiwijaya; Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar
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.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Exploring User Experience of an Interactive LMS for Green Entrepreneurship: An Empirical Study of the GEJUR Platform Kurniasari, Florentina; Wiratama, Jansen; Lestari, Elissa Dwi; Andoko, Andrey; Sony E N, Antonius; Fadhlan, Muhammad; Lala, Yohanes Brian Caesaryano; Hakim, Fayed Abdul; Mubarak, Akhmad Zainal
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.v6i4.1041

Abstract

This article aims to assess the design, development, and User Experience (UX) of GEJUR, a web-based interactive Learning Management System (LMS) designed to foster green entrepreneurship among youth in Nusa Tenggara Timur (NTT), Indonesia. This study defines green entrepreneurship as entrepreneurial activities that combine economic value creation with environmentally responsible practices that promote sustainability in business. This research enhances the domains of Human–Computer Interaction (HCI) and entrepreneurship education by illustrating how user-centered digital platforms may cultivate youth business competencies while promoting the Sustainable Development Goals (SDGs). GEJUR incorporates interactive learning modules, e-mentoring, and specific cultural features like weaved motifs (pola tenun), providing contextual relevance alongside essential LMS functions. A mixed-methods approach was employed, integrating surveys, Focus Group Discussions (FGDs), semi-structured interviews, and usability testing. Quantitative data were gathered via the System Usability Scale (SUS), whilst qualitative data documented user impressions of obstacles and anticipations. The usability evaluation, conducted with 33 individuals, resulted in a mean SUS score of 48.9, which corresponds to approximately 71.9% of the benchmark score of 68. This positions GEJUR beneath the established threshold, classified as having “poor” usability, although it remains functionally workable. Participants effectively accomplished critical tasks including authentication, course access, and quizzes, and offered favorable feedback regarding the system’s cultural integration. The results highlighted critical areas for enhancement, such as interface uniformity, minimization of response times, and more explicit user feedback. The study finds that GEJUR offers a viable yet improvable foundation for digital entrepreneurship training platforms in underprivileged areas. Subsequent study ought to broaden testing to encompass bigger and more heterogeneous user populations and implement sophisticated UX evaluation criteria beyond the SUS. Through iterative refinement, GEJUR can develop into a scalable platform that facilitates youth empowerment, sustainable business practices, and extensive socio-economic advancement.
IoT-Enabled Supervised Learning-Based Prediction Model for Smart Instrumentation Controllers in Signal Conditioning Systems Prakash, S.; Kalaiselvi, B; Batumalay, M.
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.v6i4.803

Abstract

This study proposes an intelligent Machine Learning (ML)-based smart controller for industrial flow process systems to enhance accuracy, adaptability, and robustness compared to conventional Proportional–Integral–Derivative (PID) controllers. The main idea is to replace reactive PID tuning with a proactive data-driven control strategy capable of predicting deviations and adjusting process parameters in real time. The objective is to develop and evaluate supervised learning models that can replicate and improve PID performance using real-time operational data collected from a flow process station. The proposed system integrates Internet of Things (IoT) sensors and edge computing to continuously acquire and process flow rate, pressure, and valve position data for model training and testing within the WEKA platform. Four classifiers—Linear Regression, Multilayer Perceptron (MLP), Sequential Minimal Optimization Regression (SMOreg), and M5P model tree—were compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and model-building time as key evaluation metrics. Experimental results demonstrated that the M5P pruned tree model achieved the best overall performance with an MSE of 0.0024, RMSE of 0.0577, and model-building time of only 0.03 seconds, outperforming Linear Regression (RMSE = 0.0028), MLP (RMSE = 0.026), and SMOreg (RMSE = 0.0279). The findings show that the M5P-based controller closely replicates PID behavior while offering superior predictive accuracy, faster computation, and self-adaptive learning capabilities. The novelty of this research lies in demonstrating that an IoT-enabled, data-driven smart controller can achieve real-time predictive control without requiring explicit mathematical models, thereby simplifying tuning complexities and paving the way for autonomous, scalable, and intelligent control systems in Industry 4.0 environments.
Egg Weight Estimation Based on Image Processing using Mask R-CNN and XGBoost Pardede, Jasman; Rawosi, Muhammad Fadlansyah Zikri Akhiruddin; Setyaningrum, Anisa Putri; Milenio, Rizka Milandga; Chazar, Chalifa
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.v6i4.1004

Abstract

Manually measuring egg weight in the context of livestock and the food industry can pose various problems, including time and labor requirements, the risk of egg damage, consistency and accuracy, and limitations on production scale. To address these issues, an automated egg weight estimation system is essential. This study proposes integrating computer vision and machine learning into a unified workflow that combines segmentation, classification, and regression for practical weight estimation. The proposed pipeline employs Mask R-CNN for egg segmentation, Random Forest (RF) classifier for egg type classification based on color features, and XGBoost for regression using morphological, geometric, color features, and egg type as predictors. The dataset used is 720 images, consisting of 20 eggs (10 chicken and 10 duck), each photographed from 36 rotational angles, and was collected with Ground Truth (GT) weights obtained from a digital scale. Experimental findings show that the RF classifier achieved perfect accuracy (precision, recall, and F1-score = 1.00) in distinguishing chicken and duck eggs. The XGBoost regressor obtained a training performance of MAE = 1.07 g and R² = 0.68, and a validation performance of MAE = 0.23 g and R² = 0.80 under 10-fold grouped cross-validation. Although a Support Vector Regressor baseline reached higher training accuracy (MAE = 0.22 g, R² = 0.96), it failed to generalize on validation (R² 0), highlighting XGBoost’s robustness. The feature importance analysis revealed that there are 4 (four) important features for building an estimation model, namely: Hu moments, eccentricity, elongation, and diagonal length, while color statistics played a complementary role. The novelty of this work lies in combining deep segmentation, color-based classification, and feature-driven regression into a unified framework specifically for egg weight estimation, showing its feasibility as a proof of concept and laying the foundation for future large-scale, calibrated, and externally validated deployment.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Kurniawan, Bambang; Wahyuni, Refni; Yulanda, Yulanda; Irawan, Yuda; Habib Yuhandri, 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.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Optimized AI-IoT Solution for Real-Time Pest Identification in Smart Agriculture S, Aasha Nandhini; Manoj, R. Karthick; Batumalay, M.
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.v6i4.810

Abstract

Pest detection and identification play a crucial role in reducing the damage caused by pest, insect and diseases.  Timely detection and response are essential to increase the quality and quantity of crop production. Efficient pest management strategies are important for achieving optimal crop quality and promoting sustainable agricultural practices. This research proposes a framework that can automatically detect pests and offer timely solutions to farmers. The proposed approach integrates intelligent computing methods with connected device networks to identify and classify pests in real time with high precision. The methodology focuses on efficiently segmenting the pest from the captured leaf image using a novel region growing based segmentation algorithm. The threshold for region growing based segmentation algorithm is based on the adaptive local region entropy which contributes to the efficient segmentation. Stacked Ensemble Classifier (SEC) is used for the classification. The metrics used for evaluating the performance of the pest detection framework are accuracy, Area Under the Receiver Operating Characteristic Curve, F1-Score and Mean Average Precision (mAP). The results indicate that the proposed SEC with region growing based segmentation framework achieves 98 % of classification accuracy and mAP of 0.96 proving that it is very effective in both classification and segmentation task. The comparative analysis further reveals that the SEC outperforms the existing machine learning models and ensemble learning models like majority voting and weighted average models for process innovation.
Dynamic Model for Budget Allocation in via Multi-Criteria Optimization Gulbakyt, Sembina; Almaz, Abdualiyev; Saule, Sagnayeva; Suhrab, Yoldash
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.v6i4.935

Abstract

This research introduces a dynamic multi-criteria optimization framework for fair budget distribution across four districts in Kazakhstan’s Almaty region. Its main objective is to promote transparency, equity, and efficiency in allocating a constrained regional budget of 42,656,543 thousand tenge across seven activity areas (AA): education, healthcare, transport, infrastructure, digitalization, culture, and ecology. The framework incorporates four weighted criteria: citizen satisfaction (0.2 weight), strategic development priorities (0.2), basic needs fulfillment (0.3), and urbanization level (0.3). Two optimization techniques were employed: Sequential Quadratic Programming (SQP) in MATLAB, converging in 100 iterations with an objective function value of 18,519,864.85 thousand tenge, and Genetic Algorithm (GA) in Python, achieving a slightly higher value of 18,520,000.00 thousand tenge after 500 generations. The minimal difference of 135.15 thousand tenge (0.0007% of the budget) underscores the reliability of both methods. All seven sectors received funding, with healthcare (22.05%) and transport (21.11%) allocated the largest portions, and education (7.03%) the smallest. Fairness is evidenced by a standard deviation of sectoral shares at 5.69%, a coefficient of variation of 0.398, and a Gini coefficient of 0.223. Participatory budgeting was simulated using synthetic citizen voting data derived from demographic factors. Visualizations depict the optimization process’s convergence and budget distribution across feasible solutions. A proposal for pilot testing within Kazakhstan’s e-government system (Egov) has been submitted to the Ministry of Digital Development. Future enhancements will include explainable AI, stakeholder-driven weight adjustments, and real demographic and budgetary data to foster transparency and public confidence. This framework provides a scalable, data-driven approach to participatory budgeting, harmonizing strategic objectives, socio-economic demands, and citizen preferences. SQP and GA methods achieved near-optimal solutions with objective function values of 18,519,864.85 and 18,520,000.00 thousand tenge, respectively. The 135.15 thousand tenge difference (0.0007% of the budget) is negligible, confirming their robustness.
Enhancing VIX Shock Prediction via a Probabilistic Attention Transformer Kim, Jin Su; Lee, Zoonky
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.v6i4.947

Abstract

This study proposes a Probabilistic-Attention Transformer for forecasting abrupt shifts in the Volatility Index (VIX), advancing volatility modeling by directly embedding externally estimated shock probabilities into the attention mechanism. The core idea is to modify similarity-based attention scores with daily shock probabilities derived from stochastic diffusion equations, thereby enhancing the model’s sensitivity to extreme-value dynamics. The primary objective is to improve predictive accuracy during market stress, particularly under warning (20 ≤ VIX ≤ 30) and shock (VIX 30) regimes where conventional models often fail. Using 35 years of historical VIX data (1990–2024), the framework is benchmarked against GARCH (1,1) and a standard Transformer under distinct volatility regimes. Empirical findings show that the proposed model consistently outperforms alternatives: during warning regimes, prediction error is reduced by over 40% relative to both benchmarks, while in shock regimes, improvements exceed 50%, with performance gains validated by Diebold–Mariano tests at the 1% significance level. These results demonstrate both statistical and practical significance, offering risk managers and investors more reliable forecasts during periods of heightened market instability. The contribution of this research lies in providing not only empirical evidence of improved predictive performance but also a generalizable framework for integrating probabilistic indicators into deep learning architectures. The novelty is in showing that probabilistic weighting of attention can transform standard neural architectures into early-warning systems capable of capturing regime shifts in financial markets. Beyond VIX forecasting, this methodological contribution has broader applicability to equities, exchange rates, and commodities, where identifying and responding to volatility shocks is critical for risk management and investment decision-making.
Hybrid Ensemble Learning with SMOTEENN and Soft Voting for Stunting Risk Prediction: A SHAP-Based Explainable Approach Furqany, Nuwairy El; Subianto, Muhammad; Rusyana, Asep
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.v6i4.829

Abstract

Stunting remains a critical public health concern in Indonesia, with long-term consequences for physical growth, cognitive development, and human capital. This study introduces a hybrid machine learning framework to predict household-level stunting risk by integrating Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN), soft voting ensemble, and SHapley Additive exPlanations (SHAP). The objective is to enhance both predictive accuracy and interpretability in identifying high-risk households. A dataset of 115,579 household records from West Sumatra, comprising 20 demographic, socioeconomic, health, and housing predictors, was utilized. Preprocessing steps included handling missing values, categorical encoding, and applying SMOTEENN exclusively on the training set to mitigate class imbalance. The baseline models demonstrated limited sensitivity, with XGBoost performing best at 74.56% accuracy and 71.08% F1-score on imbalanced data. After applying SMOTEENN, performance improved substantially, with XGBoost achieving 91.82% accuracy and 91.74% F1-score. Further improvements were obtained through hybridization, where the Random Forest and XGBoost soft voting ensemble reached 91.95% accuracy and 92.46% F1-score, representing a notable gain over individual classifiers. SHAP analysis added interpretability by identifying family members, education level, diverse food consumption, occupation, and drinking water source as dominant predictors of stunting risk. The novelty of this study lies in the integration of SMOTEENN with ensemble learning and SHAP, providing not only robust performance but also transparency in feature contributions. The findings demonstrate that the proposed framework improves sensitivity to minority classes, delivers superior predictive accuracy compared to baseline models, and offers interpretable insights to guide targeted interventions. By combining methodological rigor with explainability, this research contributes a practical decision-support tool for policymakers, supporting early detection of at-risk households and accelerating stunting reduction efforts in Indonesia.

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