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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 583 Documents
 55   56   57   58   59 
A Study on the Mechanism of Virtual Anchors' Interactivity and Attractiveness Influencing Consumer Trust, Emotional Attitudes, and Purchase Intentions Yu, Li Guang; Hoo, Wong Chee; Zhi, Wei; Wolor, Christian Wiradendi; Suhud, Usep
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.v7i2.1139

Abstract

This study presents a dual-mediation model examining how the interactivity and appeal of virtual streamers affect consumers' purchase intentions. The model is anchored in the "Stimulus-Organism-Response" (S-O-R) framework, seeking to clarify and compare the efficacy of two distinct psychological pathways: cognitive, mediated by perceived trust, and affective, mediated by emotional attitude. A survey of 515 Chinese consumers with prior exposure to virtual streamer e-commerce livestreams was conducted, with the model tested using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate that both interactivity and appeal significantly increase perceived trust and cultivate positive emotional attitudes. Notably, the direct effect of perceived trust on purchase intention is more potent than that of emotional attitude. Intermediary analysis further confirms that both paths are important intermediaries, and the trust intermediary path consistently exerts stronger influence. These results show that in the context of AI-driven virtual anchors, the cognitive-based trust path is more influential than the emotional-based path. By comparing these two paths, this study has improved our theoretical understanding of virtual persuasion and provided a new contribution. In fact, this study puts forward a strategy of "trust first, emotional strengthening" for marketers who use virtual anchors. Future research should investigate the cross-cultural applicability of these findings and the moderating effect of product categories.
YOLOv8-Based Microplastic Detection and Quantification in River Water Microscopic Images Pradana, Musthofa Galih; Nyamiati, Retno Dwi; Shabrina, Husna Muizzati; Adrezo, Muhammad; Maulana, Nurhuda
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.v7i2.1262

Abstract

Plastic particles with various size variations such as microplastics are environmental contaminants that are widely found in waters and have the potential to cause negative impacts. The process of identifying plastic particles using microscopic imagery manually takes a lot of time and considerable cost. In order to provide an alternative solution as part of early detection, microscopic image-based plastic particle detection was carried out with the YOLOv8 architecture, accompanied by an estimate of microplastic abundance in microplastic units per cubic meter. This study aims to develop and evaluate the detection of plastic particles in microscopic images of river water. This research dataset consists of 300 microscopic images taken from three river locations in Indonesia and annotated for model training and testing. The results of the evaluation showed that the proposed model had an aggregate performance value with a precision value of 0.786, recall of 0.66, and mAP@0.5 of 0.731. Additional test results show that with the addition of image resolution, the precision value can increase to 0.804 and the value mAP@0.5 increases to 0.762, even at the expense of computing time, which is also increasing. Extended scenario-based analysis showed that more than 87% of the detected objects fell into the category of small objects, affecting the localization sensitivity and variability of the estimated MPS value. This study also validated the results of object detection with FTIR-based laboratory tests using a full quantitative agreement between the model detection results and the identification of plastic particle materials at the sampling location level. The main contribution and findings of this study is an integrated evaluation framework for object detection, particle size characterization which is expected to be an alternative to the initial screening tool for plastic particle content.
Exploring User Acceptance of Chatbot AI: A Triangulated Framework Integrating TAM, ECTM, and TPB Constructs Putra, Agus; Wahono, Puji; Wibowo, Agus
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.v7i2.1287

Abstract

Artificial intelligence-powered chatbots have revolutionized e-commerce by providing personalized customer interactions, real-time support, and streamlined purchase processes. Despite their widespread adoption, sustained user engagement remains challenging, requiring deeper insights into cognitive, affective, and social determinants of long-term usage. This study addresses this gap by integrating the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Expectation-Confirmation Theory Model (ECTM) into a comprehensive triangulated framework to examine user acceptance and continued purchase intention toward AI chatbots in online shopping. The research investigates direct effects of confirmation, information quality, perceived usefulness (PU), perceived ease of use (PEOU), attitude, and subjective norm on satisfaction, alongside satisfaction's mediating role in predicting continued purchase intention. Data were collected from 504 respondents with prior AI chatbot experience in online shopping via purposive sampling, using validated 6-point Likert scales. Partial least squares structural equation modeling (PLS-SEM) was conducted using SmartPLS 4. Results confirm that confirmation (β=0.178, p=0.037), information quality (β=0.269, p0.001), PU (β=0.152, p=0.005), PEOU (β=0.235, p0.001), and attitude (β=0.184, p=0.001) significantly predict satisfaction, which strongly influences continued purchase intention (β=0.868, p0.001). Subjective norm exhibited no significant effect (β=-0.003, p=0.954). Satisfaction fully mediates ECTM and TAM pathways, underscoring experiential confirmation and system quality's dominance over social influences in post-adoption behavior. Theoretically, this study validates an integrated model advancing post-adoption theory in AI contexts. Practically, findings guide e-commerce platforms to enhance chatbot retention by prioritizing information accuracy, usability, and expectation alignment rather than social norms.
A Hybrid Multi-Criteria and Factorial Analysis Framework for CIRFLINK CubeSats Communication Design Evans, Warinthorn Kiadtikornthaweyot; Chuphet, Ratchanon
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.v7i2.1101

Abstract

This research presents the design and optimization of a low-cost Inter-Satellite Link for the CIRFLINK project, a 1.5U CubeSat mission aimed at advancing smart farming and disaster monitoring in rural area. To address complex design trade-offs, this study introduces a novel integrated framework combining the Analytic Hierarchy Process for qualitative prioritization and 2k factorial design for quantitative validation. The primary objective was to evaluate and optimize LoRa-based communication parameters for constrained satellite environments. Analytic Hierarchy Process results prioritized LoRa technology over optical and traditional RF subtypes due to its superior power efficiency and simplicity. Subsequently, 2k factorial experiments and ANOVA revealed that distance and physical obstruction are the dominant factors affecting performance, while parameters like Spreading Factor and Bandwidth showed less immediate impact in the tested ranges. Experimental results using ESP32 and SX1278 modules demonstrated that the Signal-to-Noise Ratio and Received Signal Strength Indicator maintain reliable connectivity, with correlation analysis showing a strong negative relationship approximate -0.913b etween distance and signal quality. Field data confirmed that the system achieves stable communication with an average SNR in line-of-sight conditions. The novelty of this work lies in the systematic fusion of multi-criteria decision-making with statistical experimental design, providing a transparent engineering roadmap for small-satellite communication. This contribution offers a validated, cost-effective ISL solution that meets mission requirements with minimal complexity, serving as a scalable model for future educational and IoT-based CubeSat constellations.
Optimizing XGBoost with Optuna for Attendance-Based Prediction of Student Academic Success Desanti, Ririn Ikana; Prasetiawan, Iwan; Suryasari, Suryasari
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.v7i2.1116

Abstract

The growth of data analytics in higher education has increased the need for predictive models that identify students’ academic potential and support data-driven decision-making. Academic institutions are able to enhance student outcomes by implementing suitable interventions that are based on an accurate early prediction of Grade Point Average (GPA). This study aims to develop a practical and accurate GPA prediction model based on Extreme Gradient Boosting (XGBoost), enhanced through Optuna-based hyperparameter tuning, to support academic monitoring systems in higher education. This model is intended to assist academic monitoring systems in higher education. Using academic performance data, attendance records, and course load information obtained from 961 undergraduate students, a quantitative predictive modeling approach was implemented. The CRISP-DM framework was implemented during the modelling process, which included the following stages: data understanding, data preparation, modelling, evaluation, and deployment. To ensure the stability and relevance of the model, exploratory analysis and correlation assessments were implemented to determine feature selection. Optuna was employed to optimize hyperparameters, utilizing Bayesian optimization with adaptive trial pruning to efficiently examine the parameter space. Experimental results demonstrate that the Optuna-tuned XGBoost model achieved superior predictive performance compared to baseline XGBoost models and models optimized using Grid Search and Random Search. The proposed model attained a coefficient of determination (R²) of 0.8637 and a Root Mean Square Error (RMSE) of 0.1165, indicating improved accuracy and robustness in handling large prediction errors. To enhance practical applicability, the final model was deployed in a Streamlit-based web application that enables real-time GPA prediction and supports academic advisors. Overall, the findings confirm that Optuna-based hyperparameter tuning significantly enhances XGBoost performance and provides a solution for data-driven academic monitoring in higher education institutions.
Adaptive Marker-Controlled Watershed Combined with Voxel Quantification for Automated Fetal Measurement Hadi, Febri; Sumijan, Sumijan; Fitri, Iskandar
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.v7i2.1224

Abstract

Accurate and consistent fetal biometric measurement is essential for assessing fetal growth and gestational age in prenatal care. However, ultrasound (US) imaging presents several challenges, including speckle noise, shadowing artifacts, and low tissue contrast, which often degrade segmentation accuracy. Classical watershed algorithms, though effective for edge detection, tend to produce over-segmentation in such complex textures. The dataset used in this study consisted of 272 ultrasound images of patients from M. Djamil Hospital, Padang, West Sumatra. The dataset covers various phases of fetal development, from the first trimester to the third trimester. All images correspond exclusively to fetal ultrasound examinations and were used solely for automated fetal biometric analysis. To overcome these issues, this study introduced an Adaptive Marker-Controlled Watershed (AMCW) algorithm combined with Voxel Quantification (VQ) to achieve more reliable and automated fetal measurements. The proposed AMCW method integrates adaptive marker generation based on morphological gradient and local intensity statistics, enabling dynamic control of internal and external markers across varying fetal regions. After segmentation, spatially calibrated pixel-based quantification was employed to estimate the dimensional properties of segmented fetal structures. The method was applied exclusively to 2D B-mode ultrasound datasets across multiple gestational ages, targeting four key fetal parameters: Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Although the present study is limited to 2D ultrasound images, the proposed framework may be extendable to 3D ultrasound data in future research. The combination of adaptive marker-controlled watershed segmentation and voxel-based quantification presents a robust, interpretable, and computationally efficient framework for automated fetal measurement. The CNN achieved a classification accuracy of 98.75% on the independent testing dataset, indicating that the extracted biometric features contain strong discriminative information for automated fetal condition assessment. This hybrid approach minimizes operator dependency and measurement variability aligning with clinical measurement trends.
Artificial Intelligence Applications and Digital Finance Development: The Moderating Role of Human Resources and Digital Infrastructure Tuan, Huynh Cao; Phuong, Tran Thanh
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.v7i2.1247

Abstract

The rapid advancement of digital technologies has fundamentally transformed financial systems, particularly in emerging economies, where digital finance plays a critical role in enhancing financial accessibility and efficiency. Among these technologies, artificial intelligence (AI) has emerged as a strategic driver reshaping digital financial services. This study aims to investigate the direct and moderating effects of artificial intelligence applications on the development of digital finance by integrating technological, human, institutional, and innovation perspectives. A sequential mixed-methods design was employed. In the qualitative phase, semi-structured interviews were conducted with 55 experts in banking, finance, and financial technology. The sample size was determined based on theoretical saturation, which was reached when successive interviews yielded no substantially new insights regarding construct dimensions or measurement refinement. Insights from this phase were used to validate constructs and refine the measurement instrument. In the quantitative phase, survey data were collected from 700 digital banking users across 20 commercial banks in Vietnam. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the proposed hypotheses. The results indicate that the digital policy framework (β = 0.300) and the digital human resource management (β = 0.279) exhibit the largest direct effects on digital finance development, based on the relative magnitude of standardized path coefficients. Among these factors, digital policy frameworks and digital human resource management demonstrate the strongest direct impacts. More importantly, the findings confirm the moderating role of artificial intelligence applications. AI significantly strengthens the relationships between digital human resource management and digital finance development, as well as between digital technology infrastructure and digital finance development. These results indicate that AI serves not only as an independent technological driver but also as a strategic catalyst, enhancing the effectiveness of digital infrastructure and human capital. This study contributes to the digital finance and information systems literature by empirically demonstrating that artificial intelligence serves as both a determinant and a moderator of digital finance development. From a practical perspective, the findings suggest that policymakers and banking executives should prioritize AI-enabled human resource strategies and the intelligent use of digital infrastructure to accelerate the development of sustainable digital finance in emerging economies.
On the Use of Zero-Inflated Mixed Models for Count Data: A Simulation and Empirical Evidence Kurnia, Anang; Fakhriyah, Zafira; Sadik, Kusman; Handayani, Dian
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.v7i2.1303

Abstract

This paper evaluates the performance of classical count regression models (Poisson, Negative Binomial, Generalized Poisson), zero-inflated models (Zero-Inflated Poisson/ZIP, Zero-Inflated Negative Binomial/ZINB, Zero-Inflated Generalized Poisson/ZIGP), and zero-inflated mixed models (ZIPMM, ZINBMM, ZIGPMM) for over-dispersed count data, particularly due to excess zeros and unobserved heterogeneity. Using simulation and empirical studies, we evaluated the performance of the models based on their predictive capability and their ability to yield valid inferences through hypothesis testing. The simulation, replicated 1000 times, involves 27 scenarios that combine various sample sizes, proportions of zero counts, and response variable distributions. Our findings indicate that ZIGPMM and ZINBMM provide the smallest root mean square error (RMSE) values. Although the Poisson model yields a relatively small RMSE, it does not adequately account for overdispersion, leading to underestimated standard errors and potentially misleading significance tests. The negative binomial model yields dispersion estimates closest to 1, indicating good performance, whereas ZIGP, ZINB, ZIGPMM, and ZINBMM perform better when zero counts are extremely high. Empirical analysis of data on under-five mortality due to pneumonia in Java Island, Indonesia, indicates that ZINB, ZINBMM, and ZIGPMM have the smallest Akaike Information Criterion (AIC), making them the most suitable models. These models show that exclusive breastfeeding and vitamin A have no significant effect on under-five child mortality due to pneumonia, while severe malnutrition has a statistically significant impact (α=0.05).
DeepCog: Classification of Mild Cognitive Impairment Using Structural MRI S, Lavanya M; Arun, Vanishri; Dhananjaya, Shashank; M, Nandini B; Srivatsa, Anand; R, Lokesha H
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.v7i2.1301

Abstract

Early identification of Mild Cognitive Impairment (MCI) is essential for preventing or delaying the progression of severe neurodegenerative disorders. The primary objective of this study is to develop an automated and computationally efficient framework for detecting MCI using structural brain imaging. The proposed research focuses on improving early diagnostic capability through a deep learning–based classification system that analyzes structural changes in brain images. The major contribution of this work lies in combining region-focused morphometric analysis with lightweight convolutional neural network architecture to achieve accurate classification while maintaining computational efficiency suitable for clinical environments. The methodology involves extracting anatomically meaningful features from structural brain scans using a region-of-interest based morphometric approach. Brain images undergo several preprocessing procedures including skull stripping, normalization, spatial alignment and data augmentation to ensure consistency and robustness of the dataset. After preprocessing, the images are used to train a lightweight deep learning model that performs binary classification between cognitively normal subjects and individuals with MCI. The study employs a publicly available neuroimaging dataset consisting of structural brain scans and associated clinical information. Experimental results demonstrate that the proposed framework achieves strong classification performance while maintaining low computational complexity. The model achieves 88.2% subject-wise test accuracy and 0.90 cross-validation accuracy, outperforming commonly used architectures such as VGG16 (78.1%) and ResNet50 (53.7%). These findings indicate that lightweight neural networks combined with region-based anatomical analysis can effectively support automated screening of MCI. The proposed approach has potential implications for scalable clinical decision support systems and may assist neurologists in early diagnosis, timely intervention, and improved cognitive healthcare management. Future research may explore multimodal data integration and longitudinal clinical validation to further enhance diagnostic reliability.
Predicting 2000-Meter Indoor Rowing Performance Using Accessible Machine Learning Models Jaggi, Arihant Singh; Dandia, Hiren
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.v7i2.999

Abstract

The 2000-meter ergometer test is widely used to measure athlete's strength, skill and efficiency in competitive rowing. Traditional tests like 500m or 1000m rowing can be too physical and elaborate for beginners or younger rowers. This study aimed to create a simpler and data driven approach to predict 2000m rowing times using basic information like age, gender and weights. Predictions were made using machine learning models including XGBoost that were applied to data from 1,341 rowers obtained from Concept 2 Database and Miami Beach Rowing Club. The model performed better for athletes over 18 years old with gender as the most important factor followed by weight and age. Finally after rigorous model training, the model showed insightful prediction accuracy with R2=0.75, MAE=0.35 min and RMSE=0.47 min. However, cross-validation of the model showed R2=-2.04, indicating overfitting due to limited variables and data. Despite this limitation, our model offers a practical application that can help rowers set realistic goals and assist coaches in personalized training. In conclusion, the model can still be improved to improve accuracy and validation but in the current study it represents a step forward in making performance insights more accessible to rowers.

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