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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 Mixed-Methods Design for Analyzing Telemedicine Adoption: An Information Systems Approach Integrating TAM–ISS, Digital Literacy, and Usability Yusuf, Fahmi; Yulyanto, Yulyanto; Priantama, Rio
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1084

Abstract

This research employs a mixed-methods design to analyze telemedicine adoption in rural Indonesia by integrating the Technology Acceptance Model (TAM) and the Information System Success Model (ISS), extended with digital literacy and usability. A quantitative survey of 314 respondents was complemented by in-depth interviews with 50 participants and demographic analysis using chi-square and logistic regression. The quantitative findings reveal that the primary adoption construct is Usability → Perceived Ease of Use (PEOU) → Perceived Usefulness (PU) → Intention to Use (ITU) → Net Benefit (NB). Perceived usefulness emerged as the strongest predictor of both satisfaction and intention. Information Quality significantly influenced satisfaction, whereas System Quality did not, indicating that clear medical information outweighs technical system performance in shaping satisfaction. Similarly, usability directly did not affect PU but indirectly through PEOU, and digital literacy influenced PU but not PEOU. Demographic analysis confirmed that occupation was significant—students and healthcare workers acted as early adopters—while age and prior training were not, suggesting that adoption transcends generational boundaries due to the widespread penetration of the JKN Mobile platform. Qualitative insights enriched these findings by highlighting key barriers and enablers such as inconsistent interfaces, infrastructure limitations, privacy concerns, community-based socialization, and expectations for adaptive features like AI diagnostics and pharmacy integration. Overall, the research confirms that telemedicine adoption in rural Indonesia is shaped by the synergy of usability, digital literacy, information quality, and social context rather than by training or demographic variables alone.
User Interface Design for Gamification of Ethnomathematics Data/Content Based on Challenges of Local Wisdom Lokasanti, I Ketut; Divayana, Dewa Gede Hendra; Sudatha, I Gde Wawan; Warpala, I Wayan Sukra
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.987

Abstract

Not all students possess the capability to effectively solve problems in accordance with the materials they have studied, both theoretically and practically, in reference to the realities of everyday life. This phenomenon is particularly evident in many junior high schools in Bali, especially within the context of mathematics education. Consequently, there is a pressing need for innovative breakthroughs to address these challenges. One such approach involves the development of gamified mathematics learning based on local Balinese wisdom. The primary objective of this research is to demonstrate the design of a user interface for gamified ethnomathematics data/content grounded in the challenges of local Balinese wisdom. This research adopts a development approach, employing the Borg and Gall model, which focuses solely on the stages of design development, initial testing, and revisions based on the outcomes of the initial trials. The subjects involved in the initial testing of the user interface design include two informatics experts, two education specialists, 40 educational technology evaluators, and 20 public junior high school teachers in Bali, particularly from the southern region. Data collection tools utilized an instrument in the form of a questionnaire consisting of 15 items related to the design of the user interface for gamified ethnomathematics data/content based on the challenges of local Balinese wisdom. The analysis of the collected data employed quantitative descriptive techniques.The research findings indicate that the quality of the user interface design is within the 'good' category, with an average percentage of 87.19%. The contribution and the most obvious implications of this research is that it can clearly demonstrate the form of gamification user interface design. Design that contains aspects of game data/content that internalize the challenges of the reality of Balinese local wisdom so that it can improve students' critical thinking skills in solving complex problems of everyday life.
Digital Financial Literacy, Entrepreneurial Competencies and Local Government Initiatives: Keys to SMEs’ Sustainability Sumiati, Ati; Respati, Dwi Kismayanti; Lutfia, Annisa; Jati, Kuat Waluyo
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.997

Abstract

This study examines the sustainability of Small and Medium-Sized Enterprises (SMEs’) in the Special Region of Yogyakarta, Indonesia, by assessing the combined effects of local government initiatives, digital financial literacy, and entrepreneurial competencies. The objective is to determine how these factors enhance long-term business viability in an increasingly digital economy. A mixed-method design was employed. In the qualitative phase, observations and in-depth interviews with officials from the Department of Industry and Trade explored the policy environment and SMEs’’ challenges, and themes from this stage were used to refine the survey instrument. In the quantitative phase, a structured survey of 338 SME owners measured digital financial literacy, entrepreneurial competencies, and sustainability practices. Data were analyzed using partial least squares structural equation modeling, including reliability and validity assessment, model fit evaluation, and bootstrapped hypothesis testing. Results show that both digital financial literacy (β = 0.546) and entrepreneurial competencies (β = 0.367) significantly influence SME sustainability, jointly explaining over 70% of the variance. The region’s policy environment—through initiatives such as incubation programs, Jogja Mark, the Sibakul Jogja platform, QR code payment adoption, and training—plays an enabling role in this relationship. The findings emphasize that digital financial literacy has the stronger effect, highlighting the importance of secure payment use, budgeting, and record-keeping for resilience and growth. The study contributes by integrating local policy, digital literacy, and entrepreneurial competency into a single empirical model, offering novel evidence on how these dimensions interact to support SME sustainability. The results provide actionable guidance for policymakers to strengthen digital financial capacity and tailor competency programs to sectoral needs.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
Understanding User Needs for a Mobile Health Application: Insights into Fasting, Training, and Muscle Development Setiyani, Lila; Eldawati, Eldawati; Azhar, Wafiqah Yasmin; Wati, Devi Fajar; Dedih, Dedih; Hikmayanti, Hanny
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1074

Abstract

Mobile Health (mHealth) applications are increasingly used to support intermittent fasting, fitness training, and nutrition tracking. However, existing solutions remain fragmented, focusing on isolated domains without addressing users’ holistic health needs. This study aimed to explore user needs and preferences for an integrated mobile health application that combines fasting, training, and muscle development, emphasizing feature importance, usability expectations, and privacy concerns. A mixed-methods approach was used: a survey (n = 50) captured demographic profiles, feature prioritization, and usability expectations, while interviews (n = 10) explored user experiences and challenges. Quantitative data were analyzed using descriptive statistics, while qualitative interview responses were grouped into key themes through manual coding and interpretation. Results from both approaches were triangulated to strengthen the validity and reliability of findings. Users prioritized workout progression tracking (M = 4.94, SD = 0.18, 95% CI [4.89, 4.99]) and protein/macro monitoring (M = 4.20, SD = 0.42) over fasting timers (M = 2.92/5) or motivational features (M = 2.88). Usability expectations were high (Ease of Use = 6.06/7; System Capability Fit = 6.36/7), and privacy was a non-negotiable factor (M = 5.00/5). Themes revealed frustrations with incomplete exercise libraries, fragmented features, and lack of personalization. The study highlights the need for integrated, user-centered mHealth applications that unify fasting, training, and nutrition while embedding privacy-by-design principles. Future work will advance this study through prototype development and usability testing using SUS and UMUX-Lite metrics.
A Stacking Ensemble Model for Predicting Student High School Graduation Outcomes Fitriyani, Fitriyani; Alkodri, Ari Amir; Aswin, Fajar
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1067

Abstract

This study develops and evaluates machine learning models to predict high school graduation outcomes and identify at-risk students for early intervention. Using a quantitative approach, data from 1,017 students across three public high schools were analyzed, encompassing academic performance (average yearly scores), behavioral factors (attendance rates and extracurricular participation), and socio-economic background (proxied by parental occupation). A comparative modeling strategy was applied, beginning with a Decision Tree baseline and advancing to a Stacking Ensemble model that integrated three heterogeneous base learners—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree—combined through a Logistic Regression meta-model. Both models were optimized using GridSearchCV and adjusted for class imbalance between graduates (93.4%) and at-risk students (6.6%). The results showed that academic variables, particularly third-year average scores (mean = 82.6, SD = 6.4) and attendance rate (mean = 94.3%), were the strongest predictors of graduation, while socio-economic indicators had minimal impact. The Stacking Ensemble achieved a notable improvement over the Decision Tree, reaching an accuracy of 99.6%, precision of 0.909, recall of 1.000, F1-score of 0.952, and AUC of 1.000, compared to the baseline accuracy of 94.9% (F1-score = 0.519, AUC = 0.83). These findings indicate the superior predictive capability of the ensemble model in identifying students at risk of non-graduation. The study’s novelty lies in combining interpretable and high-performance models to construct a practical early-warning framework that can guide educators and policymakers in targeted academic interventions. However, the near-perfect metrics also suggest potential overfitting, emphasizing the need for validation using external datasets before broader application. Overall, this research contributes a robust, data-driven methodology for improving student retention through predictive analytics in educational settings.
Unveiling the Pathways from Parenting to Entrepreneurship: A Structural Equation Modeling Approach Adha, Maulana Amirul; Ariyanti, Nova Syafira; Atmadja, Ferry Setyadi; Riyantie, Mayang; Farliana, Nina; Ansar, Rudy
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1031

Abstract

This study investigates how parenting styles influence vocational students’ entrepreneurial intentions and career choices, considering self-efficacy and entrepreneurial attitudes as mediating variables. Using a quantitative approach, data were collected from 381 vocational school students and analyzed with Structural Equation Modeling (SEM) using AMOS 24. The participants consisted of 55.7% females and 44.3% males, representing families from low-, middle-, and high-income groups based on the 2024 Jakarta provincial minimum wage, with parents working as civil servants, private-sector employees, entrepreneurs, and others. The results indicate that authoritative parenting positively fosters entrepreneurial intentions and encourages students to pursue entrepreneurship as a career path. Furthermore, the mediating roles of self-efficacy and entrepreneurial attitudes are confirmed, providing a clearer explanation of how parenting influences entrepreneurial career decisions. The study contributes theoretically by extending models of entrepreneurial intention with family socialization factors, and practically by offering a tested framework to guide efforts in promoting entrepreneurship among vocational students.
Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection Praveen, S Phani; Panguluri, Padmavathi; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Efrizoni, Lusiana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.764

Abstract

The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
Predicting Gender from Online Dating Self-Introductions Using Machine Learning, Deep Learning, and DistilBERT Gonzalez Casanova, Lionel F.; Chen, Wen-Ju; Wei, Hsi-Sheng
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.979

Abstract

This study investigates a novel approach to automated gender classification in online dating profiles by comparing models that span traditional machine learning, deep learning, and transformer-based architectures. The dataset consists of self-introduction essays from publicly accessible repositories and enriched with psychological features (LIWC), lexical features (bag-of-words), and contextual representations (raw text). The primary objective is to evaluate predictive performance, robustness, and computational cost across these modeling strategies and to assess their trade-offs. A comprehensive preprocessing pipeline was implemented, including missing-value handling, text cleaning, LIWC feature extraction, Bag-of-Words vectorization, one-hot encoding of categorical variables, and class-imbalance mitigation through random oversampling. Text augmentation using synonym replacement was subsequently applied to increase data diversity while maintaining realistic linguistic patterns. Stratified five-fold cross-validation was used for traditional models and LIWC-only deep learning experiments, and StratifiedKFold (k = 5) was applied to LIWC + BoW configurations to ensure balanced splits. DistilBERT was fine-tuned on raw essay data using an 80/20 train–test split under GPU memory and batch-size constraints. Across three runs, DistilBERT achieved an average testing accuracy of 91% ± 1%, with precision, recall, F1-score, and ROC–AUC indicating balanced performance. A GRU trained on LIWC+BoW features reached 88.62% ± 0.53% accuracy, offering competitive results at substantially lower computational cost. An MLP trained solely on LIWC features provided a stable and interpretable baseline. Confusion matrices showed balanced predictions between male and female classes, highlighting the importance of feature representation and model selection. Overall, the findings demonstrate clear trade-offs between computational demand and semantic modeling capability. These results contribute to ongoing research on gender identification and guide future work on fairness, robustness, and explainability in AI-assisted user profiling. The study also underscores practical benefits for automated analysis of unstructured text in social and psychological applications, while recognizing ethical considerations related to non-binary and gender-fluid individuals.
Applying Structural Equation Model to Explore the Impact of Performance Management on Business Performance at Small and Medium Enterprises Thu, Trang Nguyen Thi; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1135

Abstract

This study investigates how key organizational factors shape performance management and how performance management, in turn, enhances business performance among small and medium enterprises in Vietnam. The research aims to clarify the extent to which enterprise culture, internal communication, training and development, reward systems, and digital transformation influence managerial effectiveness and organizational outcomes. Building on strategic human resource management and organizational performance theories, the study develops and validates a structural model that integrates both human and technological dimensions of enterprise performance. A mixed-methods design was employed. The qualitative phase involved in-depth interviews with managers to refine the conceptual framework and contextualize measurement indicators. The quantitative phase surveyed 864 managers from small and medium enterprises across southern Vietnam to empirically test the model. Structural equation modeling was applied to examine the relationships among core organizational factors, performance management, and business performance, as well as the moderating effect of digital transformation. The findings show that training and development exert the most substantial positive influence on performance management, followed by reward systems, internal communication, and enterprise culture. Performance management significantly enhances business performance, confirming its role as a strategic mechanism that translates organizational capabilities into improved outcomes. Digital transformation directly improves business performance and enhances the impact of performance management by enabling real-time data use, transparency, and process efficiency. Overall, the study provides empirical evidence that strengthening human resource practices and integrating digital technologies are essential for improving performance management and fostering sustainable growth among Vietnamese small and medium enterprises. The results have important implications for managers and policymakers seeking to build performance-driven, digitally adaptive business environments in emerging economies.

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