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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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An Effective Hybrid Approach for Predicting and Optimizing Business Complexity Metrics and Data Insights Syah, Rahmad B.Y; Elveny, Marischa; Ananda, Rana Fathinah; Nasution, Mahyuddin K.M; Hartono, Hartono
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.830

Abstract

This study proposes a hybrid approach for optimizing complexity prediction in the domain of business intelligence by integrating three powerful techniques: the Multi-Objective Complexity Prediction Model (MPK), Principal Component Analysis (PCA), and the XGBoost regression algorithm. The MPK model serves as a state-based simulator to capture system complexity dynamics, while PCA is employed to reduce data dimensionality and eliminate redundancy among features. Subsequently, XGBoost is used as a non-linear predictive model to estimate complexity values based on the refined input features. The results show that this hybrid approach significantly improves prediction accuracy, reduces data noise, and streamlines the modelling process. Quantitative evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R²) metric demonstrates exceptional performance, with an MAE of 0.000035, an MSE of 6.7 × 10⁻⁹, and an R² of 0.9999999. These results confirm that the integration of MPK, PCA, and XGBoost is highly effective for complexity prediction tasks and can provide accurate and insightful outcomes in business intelligence analytics.
Modeling Neuroelectrical-Microbiome Crosstalk: AI-Driven Insights into Gut-Brain Bioelectrical Signaling Fadhil, Shumoos Aziz; Radif, Mustafa; Alrammahi, Atheer Hadi
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.770

Abstract

The gut-brain axis, traditionally understood as a chemical communication network, is reconceptualized in this study as a bidirectional bioelectrical system. This paper introduces a novel framework for exploring host–microbiome interactions through neuroelectrical signaling, integrating Artificial Intelligence (AI)-based modeling with experimental insights. The objective is to assess how microbial metabolites, especially Short-Chain Fatty Acids (SCFAs) such as butyrate (1.5–3.5 mM), modulate host membrane potentials, and how these bioelectrical changes influence microbial behavior. Using a hybrid simulation platform combining Recurrent Neural Networks (RNNs) and Graph Neural Networks (GNNs), we modeled dynamic interactions within a low-inflammation gut environment. Results demonstrated that increasing butyrate concentration from 1.5 to 3.5 mM led to a depolarization of enteric neurons from –70.0 mV to –63.1 mV over 24 hours. This shift was associated with a 2.5-fold increase in microbial diversity index and a suppression of pathogenic Enterobacteriaceae. SHAP (SHapley Additive exPlanations) analysis identified butyrate concentration (+0.43) and potassium channel expression (+0.27) as top contributors to excitability enhancement. Additionally, the simulation predicted improved gut motility and increased abundance of beneficial taxa such as Bifidobacterium. These findings suggest a previously underappreciated electrical layer of gut-brain communication that complements chemical pathways. The novelty of this work lies in its systems-level approach that quantifies and predicts the reciprocal influence between microbial activity and host electrophysiology. By combining bioelectrical principles with AI-driven simulation, the study contributes a mechanistic understanding and virtual testing environment for neuroelectrical-microbiome dynamics. This research opens new avenues for non-invasive interventions—such as dietary modulation or vagus nerve stimulation—to treat microbiome-related neurological and gastrointestinal disorders.
Exploring the Determinants of User Acceptance for the Digital Diary Application in Type 1 Diabetes Management: A Structural Equation Modeling Approach Triandini, Evi; Permana, Putu Adi Guna; Hanief, Shofwan; Kuswanto, Djoko; Pamungkas, Yuri; Perwitasari, Rayi Kurnia; Hisbiyah, Yuni; Rochmah, Nur; Faizi, Muhammad
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.767

Abstract

Effective management of Type 1 Diabetes (T1D), especially in children, requires continuous monitoring and care. Digital health applications have become vital in supporting routine T1D management, including insulin delivery, glucose monitoring, nutrition, and physical activity tracking. This study investigates factors influencing user acceptance of a digital diary app designed for children with T1D and their families. Using an extended Technology Acceptance Model incorporating Trust, Perceived Risk, Perceived Enjoyment, and Social Influence, a survey was conducted with 114 participants, including parents, physicians, and dietitians. Data were analyzed using Partial Least Squares Structural Equation Modeling. Findings indicate that perceived usefulness, trust, and social influence significantly affect users' attitudes and intentions to use the app, through the accepted hypothesis that considered path coefficients and p-values. Conversely, hypothesis that shows relation between perceived ease of use, enjoyment, and risk toward intention were rejected, showing unsignificant relations toward user intention to use. Furthermore, this study recommends prioritizing robust security features, fostering user trust, and engaging social networks to enhance digital health adoption in pediatric care. Future research should further explore the roles of perceived risk and enjoyment in sustaining long-term engagement
Impact of FACTS Devices on Reactive Power Optimization in Hybrid Renewable-Grid Networks Rajasree, R.; Lakshmi, D.; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.743

Abstract

Renewable energy integration with conventional electric power networks creates power-quality and stability difficulties because of their inherent volatility. The reliability improvement of hybrid renewable-grid systems depends heavily on reactive power optimization for achieving voltage control as well as loss reduction. The research explores the application of Flexible AC Transmission System (FACTS) devices with special emphasis on Distribution Static Compensator (DSTATCOM) devices for distributing reactive power compensation at the distribution level. The optimization process utilizes Particle Swarm Optimization (PSO) because it demonstrates both quick convergence and strong abilities for global search within nonlinear systems. The PSO algorithm functions to determine the perfect settings of the DSTATCOM device that enables voltage regulation within safety bounds and improves power factor performance. The hybrid system connects PV array components with wind turbines for power management together with the main grid while dealing with fluctuating load requirements. Under optimized conditions simulation output shows that DSTATCOM reduces reactive power requirements in substantial amounts. DSTATCOM's implementation enables the system to achieve better voltage security together with diminished power losses and superior load power factor levels. Detailed research shows that DSTATCOM proves efficient while being attached to the main grid for real-time compensation operations. The PSO system enables it to function efficiently throughout changing conditions of power generation and load requirements. Smart grid efficiency along with resilience advances because of the combined operation of FACTS devices and swarm intelligence methods. Through its proposed method the system ensures lasting grid sustainability and manages renewable resources intermittency effectively for process innovation.
Applied Data Science for Analyzing the Mediating Role of Digital Transformation Influencing Banking Business Efficiency in Vietnam Huy, Nguyen Quoc; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.807

Abstract

In the context of accelerated digitalization and growing competitive pressures, commercial banks in emerging economies urgently need to enhance internal capabilities to drive innovation and performance. This study investigates how key organizational factors, including employee engagement, leadership style, corporate culture, financial capacity, and management capacity, influence creative innovation and digital transformation and how these, in turn, impact business efficiency in Vietnamese commercial banks. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research develops and empirically tests a comprehensive structural model using data collected from 942 banking professionals across 30 commercial banks in Vietnam. A mixed-methods approach was employed, combining expert interviews with a large-scale survey. Structural Equation Modeling (SEM) and bootstrapped mediation analysis were used to test 17 hypotheses. The results reveal that all five internal factors positively influence Creative Innovation (CI) and Digital Transformation (DT). Notably, employee engagement and management capacity emerged as the strongest drivers. Creative innovation exerts the most significant direct effect on business efficiency, while digital transformation plays a complementary but weaker mediating role. The findings validate a multi-layered framework linking organizational dynamics to performance, offering novel insights into how banks can align internal resources with strategic goals. This study contributes to the literature by positioning creative innovation and digital transformation as mediators between organizational capabilities and business outcomes. It also provides actionable recommendations for bank executives seeking to enhance operational efficiency through people-centered, innovation-led strategies tailored to the context of emerging markets. Finally, policymakers and bank leaders should implement digital Key Performance Indicators (KPIs), foster employee-led innovation, invest in managerial training, and align human resource incentives with transformation goals to enhance efficiency and resilience in Vietnam's banking sector.
Applied Data Science for Exploring Critical Factors Affecting Systemic Risk of Commercial Banks in Vietnam Nga, Lu Phi; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.801

Abstract

The banking system is essential in developing the Vietnamese economy, serving as a capital supply channel for the economy's production, business, and investment activities. In 2024, the banking industry faces many challenges, including global macroeconomic fluctuations, the Russia-Ukraina war, and policy changes. Therefore, the study aims to quantify the impact of these factors on systemic risk using a Structural Equation Modeling (SEM) approach. Furthermore, it seeks to provide empirical evidence and actionable policy recommendations to help mitigate systemic risks, enhance financial stability, and support socio-economic recovery and development. The methodology of this study applied a structural equation model consisting of five factors: (1) Macroeconomic environment, (2) internal factors of commercial banks, (3) legal framework and supervisory authorities, (4) globalization and financial integration, and (5) technology and financial innovation. Data were collected from 450 managers working in the banking sector and processed using Amos software. The study's novelty showed that five critical factors positively impact the systemic risk of commercial banks in Vietnam. In addition, the originality of this research includes introducing technology and financial innovation into the model, a new factor of the banking industry in the digital transformation period of banking. Moreover, the results highlight that robust and timely policy interventions are essential for mitigating systemic vulnerabilities and promoting financial stability. Finally, the practical implications of the article proposed policy recommendations to help managers and policymakers minimize systemic risks due to influences from external-internal factors contributing to socio-economic recovery and development. Finally, managers and policymakers should strengthen regulatory oversight, promote digital risk management, enhance governance practices, and ensure macroeconomic stability to mitigate systemic banking risk.
Ball and Plate System Controller Using State Observer and Geometric Approach Lefrouni, Khalid; Taibi, Saoudi
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.783

Abstract

The primary objective of this paper is to present a feedback regulator using a Luenberger observer for state estimation of the ball-plate system, which is characterized by high instability and non-linearity. The novelty of this work lies in the design of an innovative control approach that explicitly considers time delay in the feedback loop—an aspect often neglected in prior studies. The adopted methodology involves modeling the system in state space while accounting for delay, and then constructing a state-feedback observer using a geometric approach. Numerical simulations were conducted to validate the proposed design. For instance, with an observer gain of L₂ = [1.58, 1.35], the controller minimizes response time along the x-axis and remains stable for delays up to 0.6364 seconds. Similarly, along the y-axis, a gain of L₅ = [0.58, 0.27] ensures robustness even with delays up to 1.4084 seconds, while effectively reducing initial overshoot. In all tested scenarios, estimation errors converged to zero, confirming the effectiveness of the observer-based controller. These findings support future work on automatic gain tuning based on performance specifications.
Development of A Deep Learning Model for Mental Health Classification and Early Screening through Draw a Person (DAP) Test Images Nurasiah, Nurasiah; Mutiara, Achmad Benny; Yusnitasari, Tristyanti; Asmarany, Anugriaty Indah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.700

Abstract

Mental health, as defined by the World Health Organization (WHO), is a fundamental aspect of overall well-being. The increasing complexity of modern society, coupled with rising levels of competition and stress, significantly impacts individuals’ mental health. The DAP test is a psychological assessment tool that uses human figure drawings to gain insights into an individual’s personality and mental condition. YOLO (You Only Look Once) is a deep learning algorithm based on Convolutional Neural Networks (CNNs) designed for real-time object detection. This study utilizes a DAP image dataset contributed by adolescents aged 12 to 16 years to develop a model for detecting and classifying objects in DAP images using the YOLOv8 algorithm. Optimal training results were achieved after 150 epochs, yielding a Precision of 0.821, Recall of 0.799, and mAP50 of 0.88. The model evaluation demonstrated an F1-Score of 0.78, indicating a balanced performance between Precision and Recall. Psychological analysis was conducted based on symptoms extracted from the characteristics of DAP images. Mental health conditions were classified according to severity levels consisting of minor, medium, and serious, based on weighted symptomatology derived from DAP image characteristics. The successful development of this model highlights its capability to classify various mental health conditions based on psychological analysis of DAP images. The findings suggest that mental health classification using DAP test images has the potential to support early screening and psychological assessment by providing an innovative and objective approach to identifying psychological indicators.
Impact of Sample Size on the Robustness of Machine Learning Algorithms for Detecting Loan Defaults Using Imbalanced Data Kobone, Boitumelo Tryphina; Montshiwa, Tlhalitshi Volition
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.713

Abstract

This study aimed to assess the impact of sample size on the robustness of five machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Decision Trees (DT), and K-Nearest Neighbour (K-NN). Although there are data-balancing techniques that aid in addressing data imbalance, they have some limitations which are discussed in this paper. The current study continues the trend in the application of these five ML classifiers for credit default detection, but it makes a contribution by examining whether sample size increment can better their performance when they are trained using a different imbalanced loan default dataset which has not been the focus of previous studies, although most ML algorithms are known to perform well when trained with large datasets. The study used a secondary loan default imbalanced dataset from Kaggle.com, where 85% of participants made loan payments and 15% defaulted. Stratified random sampling was used to select different sample sizes starting with 2% of the total observations, followed by 5%, then 10% up to 90% of the dataset, with the dependent variable being the stratum. The study found no consistent change in the classification metrics with the change in sample size, but RF and DT achieved 100% performance regardless of sample size and are therefore recommended as the most robust to data imbalance in loan default detection. The average classification metrics for NB and K-NN ranged from 72% to 92%, and SVM produced the lowest averages which were between 69% and 75%. NB, K-NN and SVM yielded poor sensitivity rates of 0% to 53%, indicating poor loan payments prediction but they had sensitivity scores in range of 84% to 86%, indicating good loan default classification. Future studies should consider other sampling methods, deep and hybrid learning methods with comparison to RF and DT.
Multi-Label Classification of Indonesian Voice Phishing Conversations: A Comparative Study of XLM-RoBERTa and ELECTRA Hidayat, Ahmad; Madenda, Sarifuddin; Hustinawaty, Hustinawaty
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.858

Abstract

Mobile phones have become a primary means of communication, yet their advancement has also been exploited by cybercriminals, particularly through voice phishing schemes. Voice phishing is a form of social engineering fraud carried out via telephone conversations to illegally obtain personal or financial information. The complexity of voice phishing continues to increase, as a single conversation may involve multiple fraudulent schemes simultaneously, necessitating the application of multi-label classification to comprehensively identify all motives of fraud. Previous studies have predominantly utilized single-label approaches and foreign-language data, making them less relevant to the Indonesian language context and unable to produce speaker segmentation outputs for conversational analysis. This study contributes by developing a multi-label voice phishing classification system specifically for Indonesian telephone conversations to address this gap. Audio data were collected from open sources and simulated recordings, resulting in a total of 300 samples labeled into six categories: five phishing modes and one non-phishing category. The proposed system consists of a preprocessing pipeline that includes noise reduction, speaker segmentation, automatic transcription, and text cleaning to preserve the context of two-way conversations. Two machine learning models based on transformer architectures, XLM-RoBERTa and ELECTRA, are employed to identify various fraud schemes that may occur simultaneously within a single conversation. The dataset was split into training, validation, and testing sets with two division ratios for performance evaluation. Several combinations of hyperparameters were tested to obtain the most optimal model configuration. Evaluation was conducted using a supervised learning approach and various performance metrics. The experimental results show that XLM-RoBERTa achieved the highest average accuracy of 97.04 ± 1.15% and the highest average F1-score of 92.66 ± 2.59%. These results highlight the novelty of applying multi-label classification in the Indonesian language context for voice phishing detection, contributing to more effective fraud identification in real-world telephony systems.

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