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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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A Data-Driven Mixed Integer Nonlinear Programming Model for Cost-Optimal Scheduling of Perishable Production and Workforce Putri, Mimmy Sari Syah; Mawengkang, Herman; Suwilo, Saib; Tulus, Tulus
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.v6i4.1019

Abstract

This study presents a data-driven, Mixed Integer Nonlinear Programming (MINLP) framework for optimizing the multi-period production scheduling of perishable products with integrated workforce planning. Its primary novelty is the holistic integration of a continuous exponential decay function for product deterioration with dynamic workforce planning, creating a unified model that optimizes production, inventory, and labor simultaneously. This approach addresses key challenges in perishable inventory systems by treating labor as a controllable resource rather than a fixed constraint. Mathematically, the model includes nonlinear inventory balance equations with decay terms and resource-dependent capacity constraints. The objective is to minimize total operational cost, comprising production, holding, and spoilage costs. Computational experiments, based on a realistic case study, demonstrate that the proposed model reduces total system cost by 6.2% and spoilage costs by 43.2% compared to a standard heuristic benchmark. The resulting production and labor schedules align closely with demand fluctuations, supporting both economic and operational efficiency. This unified framework advances the mathematical modeling of sustainable production planning and offers a practical tool for real-world industries such as food processing and pharmaceuticals.
Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor Amirkhanov, Bauyrzhan; Kunelbayev, Murat; Issa, Sabina; Amirkhanova, Gulshat; Nurgazy, Tomiris; Zhumasheva, Ainur; Alipbeki, Ongarbek
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.v6i4.779

Abstract

This article presents the design and research of a modular horizontal tubular bioreactor for efficient biogas production based on anaerobic digestion technology. The study combines a digital twin implemented in the MATLAB/Simulink environment with a physical bioreactor equipped with a sensor and control system. The developed mathematical model describes the biochemical processes of acidogenesis and methanogenesis, the thermal regime and the sensitivity of the system to key parameters. Numerical modeling and visualization methods were used for the analysis. The experiments were carried out for 30 days at a mesophilic temperature of 37 ° C, repeated three times to increase reliability. The raw material used was a mixture of cattle manure and food waste in a 3:1 ratio, with a total volume of 60 liters. Readings from temperature, pH, and methane sensors were taken every 10 minutes. Experimental data confirmed the high efficiency of the design: removal of up to 70.5% of volatile substances and methane yield of up to 80.5%. Predictive analysis has shown that the digital twin is able to predict the behavior of the system and apply corrective actions in real time. The novelty of the work lies in the integration of a digital twin with a physical bioreactor in real time through industrial communication protocols.
AMIKOM-RECSYS: Enhancing Movie Recommender System using Large Language Model (ChatGpt), Deep Learning and Probabilistic Matrix Factorization Hanafi, Hanafi; Widowati, Anik Sri; Wahyuni, Sri Ngudi
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.v6i4.897

Abstract

E-commerce has become one of the most widely used digital applications globally, enabling personalized product discovery and purchasing. To support these services, recommender systems are essential, offering item suggestions based on user preferences. Most recommender systems rely on machine learning algorithms to estimate user-item relevance scores, often utilizing product ratings. However, a persistent challenge in this domain is the issue of data sparsity, where only a small fraction of users provides explicit ratings, leading to reduced accuracy in recommendation results. In this study, we introduce a novel hybrid recommendation algorithm, named AMIKOM-RECSYS, designed to address the sparsity problem and enhance rating prediction. Our model integrates three main components included a Large Language Model (LLM) using ChatGPT, a Transformer-based encoder (BERT), and Probabilistic Matrix Factorization (PMF). The LLM generates descriptive information about movies based on specific prompts, which is then passed to BERT to encode the content into meaningful 2D vector representations. These enriched embeddings are subsequently utilized by the PMF algorithm to predict missing user-item ratings. We evaluate the proposed model on two benchmark datasets, ML-1M and ML-10M using Root Mean Squared Error (RMSE) as the evaluation metric. The AMIKOM-RECSYS model achieved RMSE values of 0.8681 on ML-1M and 0.7791 on ML-10M under a 50:50 data split, outperforming several baseline models including CNN-PMF, LSTM-PMF, and Attention-PMF. These results highlight the effectiveness of integrating LLM and Transformer-based contextual understanding into matrix factorization frameworks. In future work, we plan to extend this framework by incorporating other matrix factorization techniques such as Singular Value Decomposition (SVD) and integrating additional sources of user information, including social media activity, to further improve recommendation performance.
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.

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